Skip to main content
Log in

Likelihood Ratio Tests for Elaborate Covariance Structures and for MANOVA Models with Elaborate Covariance Structures—A Review

  • Review Article
  • Published:
Journal of the Indian Institute of Science Aims and scope

Abstract

In this paper a review is made from the primordia of the history of likelihood ratio tests for covariance structures and equality of mean vectors through the development of likelihood ratio tests that refer to elaborate covariance structures. Relations are established among several covariance structures, taking more elaborate ones as umbrella structures and examining then their particular cases of interest. References are made to bibliography where the corresponding likelihood ratio tests are developed and the distributions of the corresponding statistics addressed. Most of the likelihood ratio test statistics for one-way manova models where the covariance matrices have elaborate structures were developed quite recently. Also for these likelihood ratio tests a similar approach is taken. Although we start with the common test that uses unstructured covariance matrices, then we go on to consider tests with more elaborate covariance structures, and subsequently we specify them to their particular cases of interest. Some special attention is also given to the so-called Wilks \(\Lambda\) statistics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1:
Figure 2:

[Adapted from Fig. 5.63 in37—reproduced with authorization from Springer Nature (License # 5010050387723 from Feb 15, 2021)].

Figure 3:
Figure 4:

[Reproduced from Fig. 1 in48—reproduced with authorization from Springer Nature (License # 5181910437805 from Sep 4, 2021)].

Figure 5:

Similar content being viewed by others

References

  1. Abate J, Valkó PP (2004) Multi-precision Laplace transform inversion. Int J Numer Methods Eng 60:979–993

    Google Scholar 

  2. Ahmad MR (2008) Analysis of high-dimensional repeated measures designs: The one- and two-sample test statistics. Ph.D. Thesis. Cuvillier Verlag, Göttingen, Germany

  3. Ahmad MR, von Rosen D (2015) Tests for high-dimensional covariance matrices using the theory of u-statistics. J Stat Comput Simul 85:2619–2631

    Google Scholar 

  4. Ahmad MR, von Rosen D, Singull M (2013) A note on mean testing for high dimensional multivariate data under non-normality. Statistica Neerlandica 67:81–99

    Google Scholar 

  5. Ahmad MR, Werner C, Brunner E (2008) Analysis of high dimensional repeated measures designs: the one sample case. Comput Stat Data Anal 53:416–427

    Google Scholar 

  6. Alberto RP, Coelho CA (2007) Study of the quality of several asymptotic and near-exact approximations based on moments for the distribution of the Wilks Lambda statistic. J Stat Plan Inference 137:1612–1626

    Google Scholar 

  7. Anderson TW (2003) An introduction to multivariate statistical analysis. J. Wiley & Sons, Hoboken, NJ

    Google Scholar 

  8. Arnold BC, Coelho CA, Marques FJ (2013) The distribution of the product of powers of independent uniform random variables. J Multivar Anal 113:19–36

    Google Scholar 

  9. Bai Z, Jiang D, Yao JF, Zheng S (2009) Corrections to LRT on large-dimensional covariance matrix by RMT. Ann Stat 37:3822–3840

    Google Scholar 

  10. Bai Z, Saranadasa H (1996) Effect of high dimension: by an example of a two sample problem. Stat Sin 6:311–329

    Google Scholar 

  11. Bartlett MS (1934) The vector representations of a sample. Math Proc Camb Philos Soc 30:327–340

    Google Scholar 

  12. Bartlett MS (1938) Further aspects of the theory of multiple regression. Math Proc Camb Philos Soc 34:33–40

    Google Scholar 

  13. Bartlett MS (1939) A note on tests of significance in multivariate analysis. Proc Camb Philos Soc 35:180–185

    Google Scholar 

  14. Basu JP, Odell PL (1974) Effect of intraclass correlation among training samples on the misclassiffication probabilities of Bayes procedure. Pattern Recognit 6:13–16

    Google Scholar 

  15. Binous H (2006) Numerical Inversion of Laplace Transforms. Wolfram Repositories and Archives . https://library.wolfram.com/infocenter/MathSource/6557/

  16. Box GEP (1949) A general distribution theory for a class of likelihood criteria. Biometrika 36:317–346

    CAS  Google Scholar 

  17. Box GEP (1950) Problems in the analysis of growth and wear curves. Biometrics 6:362–389

    CAS  Google Scholar 

  18. Broda SA, Zambrano JA (2021) On quadratic forms in multivariate generalized hyperbolic random vectors. Biometrika 108:413–424

    Google Scholar 

  19. Butler RW, Wood ATA (2002) Laplace approximations for hypergeometric functions with matrix argument. Ann Stat 30:1155–1177

    Google Scholar 

  20. Butler RW, Wood ATA (2004) A dimensional clt for non-central Wilks’ Lambda in multivariate analysis. Scand J Stat 31:585–601

    Google Scholar 

  21. Butler RW, Wood ATA (2005) Approximation of power in multivariate analysis. Stat Comput 15:281–287

    Google Scholar 

  22. Butler RW, Huzurbazar S, Booth JG (1992) Saddlepoint approximations to the generalized variance and Wilks’ statistic. Biometrika 79:157–169

    Google Scholar 

  23. Butler RW, Huzurbazar S, Booth JG (1993) Saddlepoint approximations for tests of block independence, sphericity and equal variances and covariances. JRSS Ser B 55:171–183

    Google Scholar 

  24. Cai TT, Xia Y (2014) High-dimensional sparse manova. J Multivar Anal 131:174–196

    Google Scholar 

  25. Cardeño L, Nagar DK (2001) Testing block sphericity of a covariance matrix. Divulgaciones Matematicas 9:25–34

    Google Scholar 

  26. Carroll JD (1968) Generalization of canonical correlation analysis to three or more sets of variables. Proc Ann Conv APA 76:227–228

    Google Scholar 

  27. Chen SX, Qin YL (2010) A two-sample test for high-dimensional data with applications to gene-set testing. Ann Stat 38:808–835

    Google Scholar 

  28. Chen SX, Zhang LX, Zhong PS (2010) Tests for high-dimensional covariance matrices. J Am Stat Assoc 105:810–819

    CAS  Google Scholar 

  29. Cochran WG, Hopkins CE (1961) Some classification problems with multivariate qualitative data. Biometrics 17:10–32

    Google Scholar 

  30. Coelho CA (1992) Generalized canonical analysis. Ph.D. thesis, The University of Michgan, Ann Arbor, MI, USA

  31. Coelho CA (1998) The generalized integer gamma distribution—a basis for distributions in multivariate statistics. J Multivar Anal 64:86–102

    Google Scholar 

  32. Coelho CA (2000) On two asymptotic distributions for the generalized Wilks Lambda statistic. Commun Stat Theory Meth 29:1465–1486

    Google Scholar 

  33. Coelho CA (2004) The generalized near-integer gamma distribution—a basis for ‘near-exact’ approximations to the distributions of statistics which are the product of an odd number of particular independent beta random variables. J Multivar Anal 89:191–218

    Google Scholar 

  34. Coelho CA (2017) The likelihood ratio test for equality of mean vectors with compound symmetric covariance matrices. In: Gervasi O, Murgante B, Misra S, Borruso G, Torre CM, Rocha AMAC, Taniar D, Apduhan BO, Stankova E, Cuzzocrea A (eds) Computational Science and Its Applications. Lecture Notes in Computer Science 10408, vol V. Springer, pp 20–32

  35. Coelho CA (2018) Likelihood ratio tests for equality of mean vectors with circular covariance matrices. In: Oliveira TA, Kitsos C, Oliveira A, Grilo LM (eds) Recent Studies on Risk Analysis and Statistical Modeling, Contributions to Statistics. Statistics, Springer, pp 255–269

  36. Coelho CA (2021) Testing equality of mean vectors with block-circular and block compound-symmetric covariance matrices. In: Filipiak K, Markiewicz A, von Rosen D (eds) Multivariate, multilinear and mixed linear models. Springer (in print)

  37. Coelho CA, Arnold BC (2019) Finite Form Representations for Meijer G and Fox H Functions - Applied to Multivariate Likelihood Ratio Tests Using Mathematica \(\text{\textregistered}\), Maxima and R. Contributions to Statistics. Springer, Cham, Switzerland

  38. Coelho CA, Arnold BC, Marques FJ (2010) Near-exact distributions for certain likelihood ratio test statistics. J Stat Theory Pract 4:711–725

    Google Scholar 

  39. Coelho CA, Arnold BC, Marques FJ (2015) The exact and near-exact distributions of the main likelihood ratio test statistics used in the complex multivariate normal setting. Test 24:386–416

    Google Scholar 

  40. Coelho CA, Marques FJ (2009) The advantage of decomposing elaborate hypotheses on covariance matrices into conditionally independent hypotheses in building near-exact distributions for the test statistics. Linear Algebra Appl 430:2592–2606

    Google Scholar 

  41. Coelho CA, Marques FJ (2010) Near-exact distributions for the independence and sphericity likelihood ratio test statistics. J Multivar Anal 101:583–593

    Google Scholar 

  42. Coelho CA, Marques FJ (2012) Near-exact distributions for the likelihood ratio test statistic to test equality of several variance-covariance matrices in elliptically contoured distributions. Comput Stat 27:627–659

    Google Scholar 

  43. Coelho CA, Marques FJ (2013) The multi-sample block-scalar sphericity test: exact and near-exact distributions for its likelihood ratio test statistic. Commun Stat Theory Methods 42:1153–1175

    Google Scholar 

  44. Coelho CA, Marques FJ, Nadab J, Nunes C (2022) On the distribution of the likelihood ratio test of independence for random sample size—a computational approach. J Comput Appl Math. https://doi.org/10.1016/j.cam.2021.113394

    Article  Google Scholar 

  45. Coelho CA, Marques FJ, Oliveira S (2016) Near-exact distributions for likelihood ratio statistics used in the simultaneous test of conditions on mean vectors and patterns of covariance matrices. Math Problems Eng vol 2016, Article ID 8975902, 25 pp

  46. Coelho CA, Pielaszkiewicz J (2021) The likelihood ratio test of equality of mean vectors with a doubly exchangeable covariance matrix. In: Arnold BC, Balakrishnan N, Coelho CA (eds) Methodology and applications of statistics—a volume in honor of C.R. Rao on the occasion of his 100th birthday. Contributions to Statistics, Springer, pp 151–191

  47. Coelho CA, Roy A (2017) Testing the hypothesis of a block compound symmetric covariance matrix for elliptically contoured distributions. TEST 26:308–330

    Google Scholar 

  48. Coelho CA, Roy A (2020) Testing the hypothesis of a doubly exchangeable covariance matrix. Metrika 83:45–68

    Google Scholar 

  49. Coelho CA, Singull M (2021) Testing for double complete symmetry. In: Holgersson T, Singull MA(eds) Recent developments in multivariate and random matrix analysis. Springer, pp 17–39

  50. Cohen AM (2007) Numerical methods for Laplace transform inversion. Springer, New York

    Google Scholar 

  51. Consul PC (1967) On the exact distributions of likelihood ratio criteria for testing independence of sets of variates under the null hypothesis. Ann Math Stat 38:1160–1169

    Google Scholar 

  52. Consul PC (1967) On the exact distributions of the criterion \({W}\) for testing sphericity in a p-variate normal distribution. Ann Math Statist 38:1170–1174

    Google Scholar 

  53. Consul PC (1969) The exact distributions of likelihood criteria for different hypotheses. In: Krishnaiah PR (ed) Multivariate analysis II—proceedings of the second international symposium on multivariate analysis at Wright State University, Dayton, Ohio, June 1968. Academic Press, pp 171–181

  54. Correia BR, Coelho CA, Marques FJ (2018) Likelihood ratio test for the hyper-block matrix sphericity covariance structure—characterization of the exact distribution and development of near-exact distributions for the test statistic. REVSTAT 16:365–403

    Google Scholar 

  55. Cramér H (1937) Random variables and probability distributions, 1st edn. Cambridge Tracts in Mathematics and Mathematical Physica, No. 36. The University Press, Cambridge

  56. Cramér H (1937) Random variables and probability distributions, 2nd edn. Cambridge Tracts in Mathematics and Mathematical Physica, No. 36. The University Press, Cambridge

  57. Cramér H (1946) Mathematical Methods of Statistics. Princeton University Press, Princeton

    Google Scholar 

  58. Crump KS (1976) Numerical inversion of Laplace transforms using a fourier series approximation. J Assoc Comput Mach 23:89–96

    Google Scholar 

  59. Curtiss JH (1941) On the distribution of the quotient of two chance variables. Ann Math Stat 12:409–421

    Google Scholar 

  60. Davies B, Martin B (1979) Numerical inversion of the Laplace transform: a survey and comparison of methods. J Comput Phys 33:1–32

    Google Scholar 

  61. Davies RB (1973) Numerical inversion of a characteristic function. Biometrika 60:415–417

    Google Scholar 

  62. Davis AW (1971) Percentile approximations for a class of likelihood ratio criteria. Biometrika 58:349–356

    Google Scholar 

  63. Davis AW (1979) On the differential equation for Meijer’s \({G}^{p,0}_{p, p}\) function, and further tables of Wilks’s likelihood ratio criterion. Biometrika 66:519–531

    Google Scholar 

  64. Dempster AP (1958) A high dimensional two sample significance test. Ann Math Stat 29:995–1010

    Google Scholar 

  65. Diaz-Garcia JA, Jaimez RG, Mardia KV (1997) Wishart and pseudo-Wishart distributions and some applications to shape theory. J Multivar Anal 63:73–87

    Google Scholar 

  66. Dingfelder B, Weideman JAC (2015) An improved Talbot method for numerical Laplace transform inversion. Numer Algorithms 68:167–183

    Google Scholar 

  67. Duffy DG (1993) On the numerical inversion of laplace transform: comparison of three new methods on characteristic problems from applications. ACM Trans Math Softw 19:333–359

    Google Scholar 

  68. Durbin F (1974) Numerical inversion of Laplace transforms: an efficient improvement to Dubner and Abate’s method. Comput J 17:371–376

    Google Scholar 

  69. Elston RC, Grizzle JE (1962) Estimation of time-response curves and their confidence bands. Biometrics 18:148–159

    Google Scholar 

  70. Fang C, Krishnaiah PR, Nagarsenker BN (1982) Asymptotic distributions of the likelihood ratio test statistics for covariance structures of the complex multivariate normal distributions. J Multivar Anal 12:597–611

    Google Scholar 

  71. Feller W (1945) The fundamental limit theorems in probability. Bull Am Math Soc 51:800–832

    Google Scholar 

  72. Feller W (1967) An introduction to probability theory and its applications, vol II, 2nd edn. J. Wiley & Sons, New York

  73. Fisher RA (1922) On the mathematical foundations of theoretical statistics. Philos Trans R Soc Lond Ser A 222:309–368

    Google Scholar 

  74. Fisher RA (1925) Statistical Methods for Research Workers, 1st edn. Oliver & Boyd, Edinburgh

    Google Scholar 

  75. Fisher TJ (2009) On the testing and estimation of high-dimensional covariance matrices. Ph.D. thesis, Clemson University, Clemson, SC, USA

  76. Fisher TJ, Sun X, Gallagher CM (2010) A new test for sphericity of the covariance matrix for high dimensional data. J Multivar Anal 101:2554–2570

    Google Scholar 

  77. Fujikoshi Y, Himeno T, Wakaki H (2004) Asymptotic results of a high dimensional manova test and power comparison when the dimension is large compared to the sample size. J Jpn Stat Soc 34:19–26

    Google Scholar 

  78. Gaver DP (1966) Observing stochastic processes and approximate transform inversion. Oper Res 14:444–459

    Google Scholar 

  79. Geary RC (1944) Extension of a theorem by Harald Cramér on the frequency distribution of the quotient of two variables. J R Stat Soc 107:56–57

    Google Scholar 

  80. Ghosh S, Ayyala DN, Hellebuyck R (2021) Two-sample high dimensional mean test based on prepivots. Comput Stat Data Anal 163:107284

    Google Scholar 

  81. Gil-Pelaez J (1951) Note on the inversion theorem. Biometrika 38:481–482

    Google Scholar 

  82. Gleser LJ (1966) A note on the sphericity test. Ann Math Stat 37:464–467

    Google Scholar 

  83. Gleser LJ (1968) Correction to ‘A note on the sphericity test’. Ann Math Stat 39:684

    Google Scholar 

  84. Gleser LJ, Olkin I (1969) Testing for equality of means, equality of variances, and equality of covariances under restrictions upon the parameter space. Ann Inst Stat Math 21:33–48

    Google Scholar 

  85. Greenacre MJ (1984) Theory and applications of correspondence analysis. Academic Press Inc, London

    Google Scholar 

  86. Gregory KB, Carroll RJ, Baladandayuthapani V, Lahiri SN (2015) A two-sample test for equality of means in high dimension. J Am Stat Assoc 110:837–849

    CAS  Google Scholar 

  87. Grilo LM, Coelho CA (2007) Development and study of two near-exact approximations to the distribution of the product of an odd number of independent beta random variables. J Stat Plan Inference 137:1560–1575

    Google Scholar 

  88. Gupta AK (1971) Distribution of Wilks’ likelihood-ratio criterion in the complex case. Ann Inst Stat Math 23:77–87

    Google Scholar 

  89. Gupta AK (1976) Nonnull distribution of Wilks’ statistic for manova in the complex case. Commun Stat Simul Comput 5:177–188

    Google Scholar 

  90. Gupta AK, Nagar DK (1987) Likelihood ratio test for multisample sphericity. In: Gupta AK (ed) Advances in multivariate statistical analysis, theory and decision library, series B: mathematical and statistical methods, vol 5. Springer, Dordrecht, pp 111–139

    Google Scholar 

  91. Gupta AK, Nagar DK (1988) Asymptotic expansion of the nonnull distribution of likelihood ratio statistic for testing multisample sphericity. Commun Stat- Theory Methods 17:3145–3155

    Google Scholar 

  92. Gupta AK, Nagar DK, Mateu J, Rodriguez-Cortes FJ (2012) Percentage points of a test statistic useful in manova with structured covariance matrices. J Appl Stat Sci 20:29–41

    Google Scholar 

  93. Gupta AK, Rathie PN (1983) Nonnull distributions of Wilks’ Lambda in the complex case. Statistica 43:445–450

    CAS  Google Scholar 

  94. Gupta RD, Richards D (1979) Exact distributions of Wilks’ \({\Lambda }\) under the null and non-null (linear) hypotheses. Statistica 39:333–342

    Google Scholar 

  95. Gupta RD, Richards DSP (1983) Application of results of Kotz, Johnson and Boyd to the null distribution of Wilks’ criterion. In: Sen PK (ed) Contributions to Statistics: Essays in honour of Norman L. Johnson. North-Holland Publishing Company, pp 205–210

  96. Gurland J (1948) Inversion formulae for the distribution of ratios. Ann Math Stat 19:228–237

    Google Scholar 

  97. Helmert FR (1876) Die Genauigkeit der Formel von Peters zur Berechnung des wahrscheinlichen Beobachtungsfehlers directer Beobachtungen gleicher Genauigkeit. Astronomische Nachrichten 88:115–132

    Google Scholar 

  98. Hotelling H (1931) The generalization of Student’s ratio. Ann Math Stat 2:360–378

    Google Scholar 

  99. Hu Z, Tong T, Genton MG (2019) Diagonal likelihood ratio test for equality of mean vectors in high-dimensional data. Biometrics 75:256–267

    Google Scholar 

  100. Huynh H, Feldt LS (1970) Conditions under which mean square ratios in repeated measurements designs have exact F-distributions. J Am Stat Assoc 65:1582–1589

    Google Scholar 

  101. Imhof JP (1961) Computing the distribution of quadratic forms in normal variables. Biometrika 48:419–426

    Google Scholar 

  102. Ishii A, Yata K, Aoshima M (2021) Hypothesis tests for high-dimensional covariance structures. Ann Inst Stat Math 73:599–622

    Google Scholar 

  103. Jiang D, Jiang T, Yang F (2012) Likelihood ratio tests for covariance matrices of high-dimensional normal distributions. J Stat Plan Inference 142:2241–2256

    Google Scholar 

  104. Jiang T, Qi Y (2015) Likelihood ratio tests for high-dimensional normal distributions. Scand J Stat 42:988–1009

    Google Scholar 

  105. Jiang T, Yang F (2013) Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions. Ann Stat 41:2029–2074

    Google Scholar 

  106. Jouris GM (1968) On the non-central distributions of Wilks’ \({\Lambda }\) for tests of three hypotheses. Tech. Rep. 166, Dep. of Statistics, Purdue Univ

  107. Kabe DG (1958) Some applications of Meijer-G functions to distribution problems in statistics. Biometrika 45:578–580

    Google Scholar 

  108. Kabe DG (1962) On the exact distribution of a class of multivariate test criteria. Ann Math Stat 33:1197–1200

    Google Scholar 

  109. Kent JT, Dryden IL, Anderson CR (2000) Using circulant symmetry to model featureless objects. Biometrika 87:527–544

    Google Scholar 

  110. Khatri C, Srivastava M (1971) On exact non-null distributions of likelihood ratio criteria for sphericity test and equality of two covariance matrices. Sankhyā Ser A 33:201–206

    Google Scholar 

  111. Khatri C, Srivastava M (1974) Asymptotic expansions of the non-null distributions of likelihood ratio criteria for covariance matrices. Ann Stat 2:109–117

    Google Scholar 

  112. Klein M, Mathew T, Sinha B (2013) A comparison of statistical disclosure control methods: Multiple imputation versus noise multiplication. U.S. Census Bureau, Center for Statistical Research & Methodology, Research and Methodology Directorate, Research Report Series (Statistics #2013-02) . www.census.gov/srd/papers/ pdf/rrs2013-02.pdf

  113. Kollo T, von Rosen D (2005) Advanced Multivariate Statistics with Matrices. Springer, Berlin

    Google Scholar 

  114. Kong X, Harrar SW (2021) High-dimensional manova under weak conditions. Statistics 55:321–349

    Google Scholar 

  115. Kres H (1983) Statistical tables for multivariate analysis—a handbook with references to applications. Springer Series in Statistics. Springer, New York

    Google Scholar 

  116. Krishnaiah PR, Lee JC (1980) Likelihood ratio tests for mean vectors and covariance matrices. In: Krishnaiah PR (ed) Handbook of statistics, vol I. North Holland, pp 513–570

  117. Kshirsagar AM (1972) Multivariate analysis. Marcel Dekker Inc, New York

    Google Scholar 

  118. Kshirsagar AM (2003) Growth and wear curves. In: Khattree R, Rao CR (eds) Handbook of statistics, vol 22, Elsevier Science B.V, pp 1041–1054

  119. Kshirsagar AM, Smith WB (1995) Growth curves. Marcel Dekker, New York

    Google Scholar 

  120. Kulp RW, Nagarsenker BN (1984) An asymptotic expansion of the nonnull distribution of Wilks criterion for testing the multivariate linear hypothesis. Ann Stat 12:1576–1583

    Google Scholar 

  121. Lancaster HO (1965) The helmert matrices. Am Math Mon 72:4–12

    Google Scholar 

  122. Ledoit O, Wolf M (2002) Some hypothesis tests for the covariance matrix when the dimension is large compare to the sample size. Ann Stat 30:1081–1102

    Google Scholar 

  123. Lee JC, Chang TC, Krishnaiah PR (1977) Approximations to the distributions of the likelihood ratio statistics for testing certain structures on the covariance matrices of real multivariate normal populations. In: Krishnaiah PR (ed) Multivariate Analysis IV—Proceedings of The Fourth International Symposium on Multivariate Analysis at Wright State University, 1975, North-Holland, pp 105–118

  124. Lee JC, Kiishnaiah PR, Chang TC (1976) On the distribution of the likelihood ratio test statistic for compound symmetry. S Afr Stat J 10:49–62

    Google Scholar 

  125. Lee YS (1971) Distribution of the canonical correlations and asymptotic expansions for distributions of certain independence test statistics. Ann Math Stat 42:526–537

    Google Scholar 

  126. Lee YS (1972) Some results on the distribution of Wilks’s likelihood-ratio criterion. Biometrika 59:649–664

    Google Scholar 

  127. Leech FB, Healy MJR (1959) The analysis of experiments on growth rate. Biometrics 15:98–106

    Google Scholar 

  128. Lévy P (1925) Calcul des Probabilités. Gauthier-Villars, Paris

    Google Scholar 

  129. Li J (2021) Simple and efficient adaptive two-sample tests for high-dimensional data. Commun Stat-Theory and Methods 50:4428–4447

    Google Scholar 

  130. Lindeberg J (1922) Eine neue herleitung des exponentialgesetzes in der wahrscheinlichkeitsrechnung. Math Zeit 15:211–225

    Google Scholar 

  131. Lopes M, Jacob L, Wainwright M (2011) A more powerful two-sample test in high dimensions using random projection. Adv Neural Inf Process Syst 24:1206–1214

    Google Scholar 

  132. Mallet A (2000) Numerical Inversion of Laplace Transform. Wolfram Library Archive . https://library.wolfram.com/infocenter/MathSource/2691/

  133. Marques FJ, Coelho CA (2008) Near-exact distributions for the sphericity likelihood ratio test statistic. J Stat Plan Inference 138:726–741

    Google Scholar 

  134. Marques FJ, Coelho CA (2012) Near-exact distributions for the likelihood ratio test statistic of the multi-sample block-matrix sphericity test. Appl Math Comput 219:2861–2874

    Google Scholar 

  135. Marques FJ, Coelho CA (2013) The multisample block-diagonal equicorrelation and equivariance test. AIP Conf Proc 1558:793–796

    Google Scholar 

  136. Marques FJ, Coelho CA (2013) Obtaining the exact and near-exact distributions of the likelihood ratio statistic to test circular symmetry through the use of characteristic functions. Comput Stat 28:2091–2115

    Google Scholar 

  137. Marques FJ, Coelho CA (2015) The sphericity versus equivariance-equicorrelation test. AIP Conf Proc 1648:540009.1-540009.4

    Google Scholar 

  138. Marques FJ, Coelho CA, Arnold BC (2011) A general near-exact distribution theory for the most common likelihood ratio test statistics used in multivariate analysis. TEST 20:180–203

    Google Scholar 

  139. Mathai AM (1971) An expansion of Meijer’s \({G}\)-function and the distribution of products of independent beta variates. So Afr Stat J 5:71–90

    Google Scholar 

  140. Mathai AM (1971) On the distribution of the likelihood ratio criterion for testing linear hypotheses on regression coefficients. Ann Inst Stat Math 23:181–197

    Google Scholar 

  141. Mathai AM (1973) A few remarks about some recent articles on the exact distributions of certain multivariate test criteria, I. Ann Inst Stat Math 25:557–566

    Google Scholar 

  142. Mathai AM (1986) Hypothesis of multisample sphericity. J Sov Math 33:792–796

    Google Scholar 

  143. Mathai AM (1993) A handbook of generalized special functions for statistical and physical sciences. Oxford University Press, New York

    Google Scholar 

  144. Mathai AM, Haubold HJ (2008) Special Functions for Applied Scientists. Springer, New York

    Google Scholar 

  145. Mathai AM, Rathie PN (1970) The exact distribution of Votaw’s criteria. Ann Inst Stat Math 22:89–116

    Google Scholar 

  146. Mathai AM, Rathie PN (1971) The exact distribution of Wilks’ criterion. Ann Math Stat 42:1010–1019

    Google Scholar 

  147. Mathai AM, Saxena RK (1973) Generalized Hypergeometric Functions with Applications in Statistics and Physical Sciences, Lecture Notes in Mathematics, vol 348. Springer, New York

    Google Scholar 

  148. Mauchly JW (1940) Significance test for sphericity of a normal \(n\)-variate distribution. Ann Math Stat 11(2):204–209

    Google Scholar 

  149. Meijer CS (1941) Multiplikationstheoreme für die Funktion \({G}_{mn}^{pq}(z)\). Proc. Koninklijk Nederlandse Akademie van Weteenschappen 44:1062–1070

    Google Scholar 

  150. Meijer CS (1946) On the \({G}\)-function i-viii. Proc Koninklijk Nederlandse Akademie van Weteenschappen 49:227–237, 344–356, 457–469, 632–641, 765–772, 936–943, 1063–1072, 1165–1175

  151. Mendoza JL (1980) A significance test for multisample sphericity. Psychometrika 45:495–498

    Google Scholar 

  152. Montella C, Diard JP (2015) Comparing four methods of numerical inversion of laplace transforms (NILT). Wolfram Demonstrations Project. https://bit.ly/Comparing_Four_Methods_of_Numerical_Inversion_of_Laplace_Transf

  153. Moschopoulos PG (1988) Asymptotic expansions of the non-null distribution of the likelihood ratio criterion for multisample sphericity. Am J Math Manag Sci 8:135–163

    Google Scholar 

  154. Moschopoulos PG (1992) The hypothesis of multisample block sphericity. Sankhyā Ser A 54:260–270

    Google Scholar 

  155. Moura R, Klein M, Coelho CA, Sinha B (2017) Inference for multivariate regression model based on synthetic data generated under Fixed-Posterior Predicitve sampling: comparison with Plug-in sampling. REVSTAT-Stat J 15:155–186

    Google Scholar 

  156. Moura R, Klein M, Zylstra J, Coelho CA, Sinha B (2021) Inference for Multivariate Regression model based on synthetic data generated using Plug-in sampling. J Am Stat Assoc 116:720–733

    CAS  Google Scholar 

  157. Moura R, Sinha B, Coelho CA (2017) Inference for multivariate regression model based on multiply imputed synthetic data generated via Posterior Predictive sampling. AIP Conf Proc 1836:020065-1-020065–6

    Google Scholar 

  158. Muirhead RJ (2005) Aspects of multivariate statistical theory, 2nd edn. J. Wiley & Sons, Hoboken, New Jersey

    Google Scholar 

  159. Mukherjee BN, Maiti SS (1988) On some properties of positive definite to eplitz matrices and their possible applications. Linear Algebra Appl 102:211–240

    Google Scholar 

  160. Nagao H (1970) Asymptotic expansions of some test criteria for homogeneity of variances and covariance matrices from normal populations. J Sci Hiroshima Univ Ser A-I 34:153–247

    Google Scholar 

  161. Nagao H (1972) Non-null distributions of the likelihood ratio criteria for independence and equality of mean vectors and covariance matrices. Ann Inst Stat Math 24:67–79

    Google Scholar 

  162. Nagao H (1973) Nonnull distributions of two test criteria for independence under local alternatives. J Multivar Anal 3:435–444

    Google Scholar 

  163. Nagao H (1973) On some test criteria for covariance matrix. Ann Stat 1:700–709

    Google Scholar 

  164. Nagar DK, Castañeda ME (2003) Testing multisample compound symmetry. Adv Appl Stat 3:285–292

    Google Scholar 

  165. Nagar DK, Castañeda ME (2006) Distribution and percentage points of LRC for testing multisample compound symmetry in the bivariate and the trivariate cases. Metron 64:217–238

    Google Scholar 

  166. Nagar DK, Gupta AK (1996) On a test statistic useful in manova with structured covariance matrices. J Appl Stat Sci 4:185–202

    Google Scholar 

  167. Nagar DK, Jain SK, Gupta AK (1985) Distribution of LRC for testing sphericity of a complex multivariate Gaussian model. J Math Math Sci 8:555–562

    Google Scholar 

  168. Nagarsenker BN (1975) Percentage points of WiLks’ \({L}_{vc}\) criterion. Comm Stat 4:629–641

    Google Scholar 

  169. Nagarsenker BN, Das MM (1975) Exact distribution of sphericity criterion in the complex case and its percentage points. Commun Stat-Theory Methods 4:363–374

    Google Scholar 

  170. Nagarsenker BN, Nagarsenker PB (1981) Distribution of the likelihood ratio statistic for testing sphericity structure for a complex normal covariance matrix. Sankhyā 34:352–359

    Google Scholar 

  171. Nagarsenker BN, Pillai KCS (1973) The distribution of the sphericity test criterion. J Multivar Anal 3:226–235

    Google Scholar 

  172. Nair US (1939) The application of the moment function in the study of distribution laws in statistics. Biometrika 30:274–294

    Google Scholar 

  173. Nandi B (1977) The exact null distribution of Wilks’ criterion. Sankhyā Ser B 39:307–315

    Google Scholar 

  174. Narain RD (1950) On the incomplete unbiased character of tests of independence in multivariate normal systems. Ann Math Stat 21:293–298

    Google Scholar 

  175. Neyman J, Pearson ES (1928) On the use and interpretation of certain test criteria for purposes of statistical inference, part i. Biometrika 20A:175–240

    Google Scholar 

  176. Ohlson M, Srivastava MS (2010) Profile analysis for a growth curve model. J Jpn Stat Soc 40:1–21

    Google Scholar 

  177. Olkin I (1973) Testing and estimation for structures which are circularly symmetric in blocks. In: Kabe DG, Gupta RP (eds) Multivariate statistical inference. North-Holland, Amsterdam, pp 183–195

    Google Scholar 

  178. Olkin I, Press SJ (1968) Testing and estimation for a circular stationary model. Tech. rep., Tech. Rep. 31/68, Dep. of Statistics, Stanford University, C.A

  179. Olkin I, Press SJ (1969) Testing and estimation for a circular stationary model. Ann Math Stat 40:1358–1373

    Google Scholar 

  180. O’Neill ME (1978) Asymptotic distributions of the canonical correlations from contingency tables. Aust J Stat 20:75–82

    Google Scholar 

  181. O’Neill ME (1978) Distributional expansions for canonical correlations from contingency tables. J R Stat Soc B 40:303–312

    Google Scholar 

  182. O’Neill ME (1981) A note on the canonical correlations from contingency tables. Aust J Stat 23:58–66

    Google Scholar 

  183. Panik MJ (2014) Growth Curve Modeling. J. Wiley & Sons, Hoboken, New Jersey

    Google Scholar 

  184. Pauly M, Ellenberger D, Brunner E (2015) Analysis of high-dimensional one group repeated measures designs. Statistics 49:1243–1261

    Google Scholar 

  185. Pearson ES, Wilks SS (1933) Methods of statistical analysis appropriate for \(k\) samples of two variables. Biometrika 25:353–378

    Google Scholar 

  186. Perlman MD (1980) Unbiasedness of the likelihood ratio tests for equality of several covariance matrices and equality of several normal populations. Ann Stat 8:247–263

    Google Scholar 

  187. Piessens R (1971) Gaussian quadrature formulas for the numerical integration of Bromwich’s integral and the inversion of the Laplace transform. J Eng Math 5:1–9

    Google Scholar 

  188. Piessens R (1973) Algorithm 453-gaussian quadrature formulas for Bromwich’s integral. Commun Assoc Comput Mach 16:486–487

    Google Scholar 

  189. Piessens R (1975) A bibliography on numerical inversion of the Laplace transform and applications. J Comput Appl Math 1:115–126

    Google Scholar 

  190. Piessens R, Dang NDP (1976) A bibliography on numerical inversion of the Laplace transform and applications: a supplement. J Comput Appl Math 2:225–228

    Google Scholar 

  191. Pillai KCS, Al-Ani S, Jouris GM (1969) On the distributions of the ratios of the roots of a covariance matrix and Wilks’ criterion for tests of three hypotheses. Ann Math Stat 40:2033–2040

    Google Scholar 

  192. Pillai KCS, Gupta AK (1969) On the exact distribution of Wilks’s criterion. Biometrika 56:109–118

    Google Scholar 

  193. Pillai KCS, Jouris GM (1971) Some distribution problems in the multivariate complex Gaussian case. Ann Math Stat 42:517–525

    Google Scholar 

  194. Pillai KCS, Nagarsenker BN (1971) On the distribution of the sphericity test criterion in classical and complex normal populations having unknown covariance matrix. Ann Math Stat 42:764–767

    Google Scholar 

  195. Popović BV, Mijanović A, Witkovský V (2021) Computing the exact distribution of a linear combination of generalized logistic random variables and its applications. J Stat Comput Simul First on-line. https://doi.org/10.1080/00949655.2021.1982942

    Article  Google Scholar 

  196. Post EL (1930) Generalized differentiation. Trans Am Math Soc 32:723–781

    Google Scholar 

  197. Potthoff RF, Roy SN (1964) A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51:313–326

    Google Scholar 

  198. Rao CR (1948) Tests of significance in multivariate analysis. Biometrika 35:58–79

    CAS  Google Scholar 

  199. Rao CR (1951) An asymptotic expansion of the distribution of Wilks’s criterion. Bull Int Stat Inst 33:177–180

    Google Scholar 

  200. Rao CR (1958) Some statistical methods for comparison of growth curves. Biometrics 14:1–17

    Google Scholar 

  201. Rencher AC (2002) Methods of multivariate analysis, 2nd edn. Wiley, New York

    Google Scholar 

  202. Rencher AC, Christensen WF (2012) Methods of multivariate analysis, 3rd edn. Wiley, New York

    Google Scholar 

  203. Rizzardi M (1995) A modification of Talbot’s method for the simultaneous approximation of several values of the inverse Laplace transform. ACM Trans Math Softw 21:347–371

    Google Scholar 

  204. Rodriguez-Cortes FJ, Nagar DK (2007) Percentage points for testing equality of mean vectors. J Nigerian Math Soc 26:85–97

    Google Scholar 

  205. von Rosen D (2018) Bilinear regression analysis—an introduction. Lecture Notes in Statistics. Springer, Cham, Switzerland

    Google Scholar 

  206. Roy J, Murthy VK (1960) Percentage points of Wilks’ \({L}_{mvc}\) and \({L}_{vc}\) criteria. Psychometrika 25:243–250

    Google Scholar 

  207. Schatzoff M (1966) Exact distributions of Wilks’s likelihood ratio criterion. Biometrika 53:347–358

    Google Scholar 

  208. Schorr B (1975) Numerical inversion of a class of characteristic functions. BIT Numer Math 15:94–102

    Google Scholar 

  209. Schott JR (2007) Some high-dimensional tests for a one-way manova. J Multivar Anal 98:1825–1839

    Google Scholar 

  210. Sengupta A (2004) Generalized Canonical Variables. Wiley StatsRef, Statistical Reference Online., Wiley, New York. https://onlinelibrary.wiley.com/doi/10.1002/9781118445112.stat02670

  211. Shen Y, Lin Z (2014) Tests for a multiple-sample problem in high dimensions. Commun Stat-Theory Methods 43:291–305

    Google Scholar 

  212. Shephard NG (1991) From characteristic function to distribution function: A simple framework for the theory. Econometric Theory 7:519–529

    Google Scholar 

  213. Singh A (1982) Exact distribution of Wilks’ Lvc criterion and its percentage points in the complex case. Commun Stat-Simul Comput 11:217–225

    Google Scholar 

  214. Srivastava MS (2005) Some tests concerning the covariance matrix in high dimensional data. J Jpn Stat Soc 35:251–272

    Google Scholar 

  215. Srivastava MS (2007) Multivariate theory for analysing high dimensional data. J Jpn Stat Soc 37:53–86

    Google Scholar 

  216. Srivastava MS, Du M (2008) A test for the mean vector with fewer observations than the dimension. J Multivar Anal 99:386–402

    Google Scholar 

  217. Srivastava MS, Fujikoshi Y (2006) Multivariate analysis of variance with fewer observations than the dimension. J Multivar Anal 97:1927–1940

    Google Scholar 

  218. Srivastava MS, Katayama S, Kano Y (2013) A two sample test in high dimension with fewer observations than the dimension. J Multivar Anal 114:349–358

    Google Scholar 

  219. Srivastava MS, Kollo T, von Rosen D (2011) Some tests for the covariance matrix with fewer observations than the dimension under non-normality. J Multivar Anal 102:1090–1103

    Google Scholar 

  220. Srivastava MS, Kubokawa T (2013) Tests for multivariate analysis of variance in high dimension under non-normality. J Multivar Anal 115:204–216

    Google Scholar 

  221. Srivastava MS, von Rosen D (1999) Growth curve models. In: Ghosh S (ed.) Multivariate Analysis, Design of Experiments, and Survey Sampling, pp 571–602. Marcel Dekker

  222. Srivastava MS, Singull M (2017) Test for the mean matrix in a growth curve model for high dimensions. Commun Stat-Theory Methods 46:6668–6683

    Google Scholar 

  223. Srivastava MS, Singull M (2017) Testing sphericity and intraclass covariance structures under a growth curve model in high dimension. Commun Stat-Simul Comput 46:5740–5751

    Google Scholar 

  224. Srivastava MS, Yau WK (1989) Saddlepoint method for obtaining tail probability of Wilks’ likelihood ratio test. J Multivar Anal 31:117–126

    Google Scholar 

  225. Srivastava R, Li P, Ruppert D (2016) RAPTT: an exact two-sample test in high dimensions using random projections. J Comput Graphical Stat 25:954–970

    Google Scholar 

  226. Stehfest H (1970) Algorithm 368: numerical inversion of Laplace transforms. Commun ACM 13:47–49

    Google Scholar 

  227. Sugiura N (1969) Asymptotic expansions of the distributions of the likelihood ratio criteria for covariance matrix. Ann Math Stat 40:2051–2063

    Google Scholar 

  228. Sugiura N (1971) Asymptotic solutions of the hypergeometric function 1f1 of matrix argument, useful in Multivariate Analysis. Ann Inst Stat Math 24:517–524

    Google Scholar 

  229. Sugiura N (1973) Further asymptotic formulas for the non-null distributions of three statistics for multivariate linear hypothesis. Ann Inst Stat Math 25:153–163

    Google Scholar 

  230. Sugiura N, Fujikoshi Y (1969) Asymptotic expansions of the non-null distributions of the likelihood ratio criteria for mluitvariate linear hypothesis and independence. Ann Math Stat 40:942–952

    Google Scholar 

  231. Sugiura N, Nagao H (1968) Unbiasedness of some test criteria for the equality of one or two covariance matrices. Ann Math Stat 39:1686–1692

    Google Scholar 

  232. Szatrowski TH (1976) Estimation and testing for block compound symmetry and other patterned covariance matrices with linear and non-linear structure. Ph.D. thesis, Stanford University, Stanford, CA, USA

  233. Szatrowski TH (1979) Asymptotic nonnull distributions for likelihood ratio statistics in the multivariate Normal patterned mean and covariance matrix testing problem. Ann Stat 7:823–837

    Google Scholar 

  234. Szatrowski TH (1982) Testing and estimation in the block compound symmetry problem. J Educ Stat 7:3–18

    Google Scholar 

  235. Talbot A (1979) The accurate numerical inversion of Laplace transforms. IMA J Appl Math 23:97–120

    Google Scholar 

  236. Tang J, Gupta AK (1986) Exact distribution of certain general test statistics in multivariate analysis. Aust J Stat 28:107–114

    Google Scholar 

  237. Timm NH (2002) Applied multivariate analysis. Springer Texts in Statistics, Springer, New York

    Google Scholar 

  238. Tonda T, Fujikoshi Y (2004) Asymptotic expansion of the null distribution of lr statistic for multivariate linear hypothesis when the dimension is large. Commun Stat-Theory Methods 33:1205–1220

    Google Scholar 

  239. Tretter MJ, Walster W (1975) Central and noncentral distributions of Wilks’ statistic in manova as mixtures of incomplete beta functions. Ann Stat 3:467–472

    Google Scholar 

  240. Tukey JW, Wilks SS (1946) Approximation of the distribution of the product of beta variables by a single beta variable. Ann Math Stat 17:318–324

    Google Scholar 

  241. Valko P (2019) NInverseLaplaceTransform. Wolfram Function Repository .https://resources.wolframcloud.com/FunctionRepository/resources/NInverseLaplaceTransform/

  242. Vonesh EF, Chinchilli VM (1996) Linear and nonlinear models for the analysis of repeated measurements. Marcel Dekker, New York

    Google Scholar 

  243. Votaw DF (1948) Testing compound symmetry in a normal multivariate distribution. Ann Math Stat 19:447–473

    Google Scholar 

  244. Wald A, Brookner RJ (1941) On the distribution of Wilks’ statistic for testing the independence of several groups of variates. Ann Math Stat 12:137–152

    Google Scholar 

  245. Wall FJ (1967) The generalized variance ratio or U-statistic. The Dikewood Corporation, Albuquerque, New Mexico

    Google Scholar 

  246. Waller LA, Turnbull BW, Hardin JM (1995) Obtaining distribution functions by numerical inversion of characteristic functions with applications. Am Stat 49:346–350

    Google Scholar 

  247. Walster GW, Tretter MJ (1980) Exact noncentral distributions of Wilks’ \({\Lambda }\) and Wilks-Lawley \(u\) criteria as mixtures of incomplete beta functions for three tests. Ann Stat 8:1388–1390

    Google Scholar 

  248. Weber H (1974) Numerische inversion der Laplace- und Fourier-transformation mit Laguerre-funktionen. Master’s thesis, University of Mainz, Germany

  249. Weber H (1981) Numerical computation of the Fourier transform using Laguerre functions and the Fast Fourier Transform. Numerische Mathematik 36:179–209

    Google Scholar 

  250. Weeks WT (1966) Numerical inversion of Laplace transforms using Laguerre functions. J Assoc Comput Mach 13:419–426

    Google Scholar 

  251. Widder DV. The Laplace transform. Princeton Mathematical Series. Princeton University Press, Princeton, NJ (1941, 1st printing; 1946, 2nd printing)

  252. Wilks SS (1932) Certain generalizations in the analysis of variance. Biometrika 24:471–494

    Google Scholar 

  253. Wilks SS (1934) Moment-generating operators for determinants of product moments in samples from a normal system. Ann Math 35:312–340

    Google Scholar 

  254. Wilks SS (1935) The likelihood test of independence in contingency tables. Ann Math Stat 6:190–196

    Google Scholar 

  255. Wilks SS (1935) On the independence of \(k\) sets of normally distributed statistical variables. Econometrica 3(3):309–326

    Google Scholar 

  256. Wilks SS (1938) The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann Math Stat 9:60–62

    Google Scholar 

  257. Wilks SS (1946) Sample criteria for testing equality of means, equality of variances, and equality of covariances in a normal multivariate distribution. Ann Math Stat 17:257–281

    Google Scholar 

  258. Wishart J (1928) The generalised product moment distribution in samples from a normal multivariate populationthe generalised product moment distribution in samples from a normal multivariate population. Biometrika 20A:32–52

    Google Scholar 

  259. Wishart J (1938) Growth rate determinations in nutrition studies with the bacon pig, and their analysis. Biometrika 30:16–28

    Google Scholar 

  260. Witkovský V (2001) Computing the distribution of a linear combination of inverted gamma variables. Kybernetika 37(1):79–90

    Google Scholar 

  261. Witkovský V (2001) On the exact computation of the density and of the quantiles of linear combinations of \(t\) and F random variables. J Stat Plan Inference 94(1):1–13

    Google Scholar 

  262. Witkovský V (2004) Matlab algorithm TDIST: The distribution of a linear combination of student’s \(t\) random variables. In: Antoch J (ed) COMPSTAT 2004 Symposium, Proceedings in Computational Statistics, pp 1995–2002. Physica-Verlag/Springer 2004, Heidelberg, Germany

  263. Witkovský V (2020) CharFunTool: The characteristic functions toolbox (matlab). https://github.com/witkovsky/CharFunTool

  264. Witkovský V (2020) Computing the exact distribution of the Bartlett’s test statistic by numerical inversion of its characteristic function. J Appl Stat 47(13–15):2749–2764

    Google Scholar 

  265. Witkovský V, Wimmer G, Duby T (2020) Estimating the distribution of a stochastic sum of iid random variables. Mathematica Slovaca 70(3):759–774

    Google Scholar 

  266. Wu Y, Genton MG, Stefanski LA (2006) A multivariate two-sample mean test for small sample size and missing data. Biometrics 62:877–885

    Google Scholar 

  267. Xue K, Yao F (2020) Distribution and correlation-free two-sample test of high-dimensional means. Ann Stat 48:1304–1328

    Google Scholar 

  268. Yamada T, Srivastava MS (2012) A test for the multivariate analysis of variance in high-dimension. Commun Stat Theory Methods 41:2602–2615

    Google Scholar 

  269. Yang F (2011) Likelihood ratio tests for high-dimensional normal distributions. Ph.D. thesis, The University of Minnesota, Minneapolis, MN, USA

  270. Yang X, Zheng X, Chen J (2021) Testing high-dimensional covariance matrices under the elliptical distribution and beyond. J Econ 221:409–423

    Google Scholar 

  271. Yi L, Xie J (2018) A high-dimensional likelihood ratio test for circular symmetric covariance structure. Commun Stat-Theory Methods 47:1392–1402

    Google Scholar 

  272. Zakian V (1975) Properties of \(i_{mn}\) and \(j_{mn}\) approximants and applications to numerical inversion of Laplace transforms and initial value problems. J Math Anal Appl 50:191–222

    Google Scholar 

  273. Zhang H, Wang H (2021) A more powerful test of equality of high-dimensional two-sample means. Comput Stat Data Anal 164:107318

    Google Scholar 

  274. Zhang JT, Guo J, Zhou B (2017) Linear hypothesis testing in high-dimensional one-way manova. J Multivar Anal 155:200–216

    Google Scholar 

  275. Zhang JT, Guo J, Zhou B, Cheng MY (2020) A simple two-sample test in high dimensions based on L2-norm. J Am Stat Assoc 115:1011–1027

    CAS  Google Scholar 

  276. Zhu T, Zhang JT (2021) Linear hypothesis testing in high-dimensional one-way manova: a new normal reference approach. Comput Stat. https://doi.org/10.1007/s00180-021-01110-6

    Article  Google Scholar 

Download references

Acknowledgements

The author wants to thank the comments and suggestions of two anonymous reviewers which helped in improving the original manuscript.

Funding

This work is funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the project UIDB/00297/2020 (Center for Mathematics and Applications CMA-FCT/UNL).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos A. Coelho.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coelho, C.A. Likelihood Ratio Tests for Elaborate Covariance Structures and for MANOVA Models with Elaborate Covariance Structures—A Review. J Indian Inst Sci 102, 1219–1257 (2022). https://doi.org/10.1007/s41745-022-00300-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41745-022-00300-5

Keywords

Mathematics Subject Classification

Navigation