Journal of Soviet Mathematics

, Volume 50, Issue 3, pp 1643–1684

Probabilistic-statistical programs from “applied statistics”

  • G. V. Martynov
Article

Abstract

A survey is made of 227 papers contained in the section on algorithms in the journal “Applied Statistics” and published in 1968–1987. The papers contain descriptions of statistical algorithms and the texts of the corresponding programs in the languages FORTRAN and ALGOL. The programs realize methods relating to the estimation of parameters, testing hypotheses, regression and variance analysis, planning of experiments, analysis of time series, calculation of distribution functions, etc.

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Literature cited

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  57. 57.
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  71. 71.
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  72. 72.
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  73. 73.
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  74. 74.
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  75. 75.
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  76. 76.
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  78. 78.
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  79. 79.
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  80. 80.
    J. F. Gentleman, “Algorithm AS 130. Moving statistics for enhanced scatter plots,” Appl. Statist.,27, No. 3, 354–358 (1978).Google Scholar
  81. 81.
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  82. 82.
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  83. 83.
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  85. 85.
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  86. 86.
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  87. 87.
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  88. 88.
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  89. 89.
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  90. 90.
    P. Griffiths and I. D. Hills (eds.), Applied Statistics Algorithms, Ellis Hoorwood Ltd., Chichester (1985).Google Scholar
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  99. 99.
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  100. 100.
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  101. 101.
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  102. 102.
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  103. 103.
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  104. 104.
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  105. 105.
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  106. 106.
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  107. 107.
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  108. 108.
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  109. 109.
    I. D. Hill, “Algorithm AS 100. Normal-Johnson and Johnson-normal transformations,” Appl. Statist.,25, No. 2, 190–192 (1976).Google Scholar
  110. 110.
    I. D. Hill, R. Hill, and R. L. Holder, “Algorithm AS 99. Fitting Johnson curves by moments,” Appl. Statist.,25, No. 2, 180–189 (1976).Google Scholar
  111. 111.
    I. D. Hill and R. Peto, “Algorithm AS 35. Probabilities derived from finite populations,” Appl. Statist.,20, No. 1, 99–105 (1971).Google Scholar
  112. 112.
    T. R. Hopkins, “Algorithm AS 193. A revised algorithm for the spectral test,” Appl. Statist.,32, No. 3, 328–335 (1983).Google Scholar
  113. 113.
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  114. 114.
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  115. 115.
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  116. 116.
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  117. 117.
    M. D. Krailo and M. C. Pike, “Algorithm AS 196. Conditional multivariate logistic analysis of stratified case-control studies,” Appl. Statist.,33, No. 1, 95–103 (1984).Google Scholar
  118. 118.
    W. J. Krzanowski, “Algorithm AS 28. Transposing multiway structures,” Appl. Statist.,19, No. 1, 115–118 (1970).Google Scholar
  119. 119.
    S. W. Lagakos and M. H. Kuhns, “Algorithm AS 125. Maximum likelihood estimation for censored exponential survival data with covariates,” Appl. Statist.,27, No. 2, 190–197 (1978).Google Scholar
  120. 120.
    V. K. Lagoo, “Algorithm AS 25. Classification of means from analysis of variance,” Appl. Statist.,18, No. 3, 294–298 (1969).Google Scholar
  121. 121.
    D. P. Laurie, “Algorithm AS 175. Cramér-Wold factorization,” Appl. Statist.,31, No. 1, 86–93 (1982).Google Scholar
  122. 122.
    B. L. Leathers, “Algorithm AS 114. Computing the numerator of association when the data are ordered categories,” Appl. Statist.,26, No. 2, 211–213 (1977).Google Scholar
  123. 123.
    B. L. Leathers, “Algorithm AS 119. Tabulating sparse joint frequency distributions,” Appl. Statist.,26, No. 3, 364–368 (1977).Google Scholar
  124. 124.
    B. L. Leathers, “Algorithm AS 131. Tabulating frequency distributions for variables with structured code sets,” Appl. Statist.,27, No. 3, 359–362 (1978).Google Scholar
  125. 125.
    Tze-San Lee, “Algorithm AS 223. Optimum ridge parameter selection,” Appl. Statist.,36, No. 1, 112–118 (1987).Google Scholar
  126. 126.
    F. B. Leech, “Algorithm AS 33. Calculation of hypergeometric sample sizes,” Appl. Statist.,19, No. 3, 287–289 (1970).Google Scholar
  127. 127.
    R. V. Lenth, “Algorithm AS 226. Computing of hypergeometric sample sizes,” Appl. Statist.,36, No. 2, 241–244 (1987).Google Scholar
  128. 128.
    B. Leventhal, “Algorithm AS 107. Operating characteristics and average sampling number for a general class of sequential sampling plans,” Appl. Statist.,26, No. 1, 98–106 (1977).Google Scholar
  129. 129.
    P. Lin Shang, “Algorithm AS 185. Automatic model selection in contingency tables,” Appl. Statist.,31, No. 3, 317–326 (1982).Google Scholar
  130. 130.
    P. Lin Shang and R. B. Bendel, “Algorithm AS 213. Generation of population correlation matrices with specified eigenvalues,” Appl. Statist.,34, No. 2, 193–198 (1985).Google Scholar
  131. 131.
    R. E. Lund, “Algorithm AS 152. Cumulative hypergeometric probabilities,” Appl. Statist.,29, No. 2, 221–223 (1980).Google Scholar
  132. 132.
    R. E. Lund and J. R. Lund, “Algorithm AS 190. Probabilities and upper quantiles for the Studentized range,” Appl. Statist.,32, No. 2, 204–210 (1983).Google Scholar
  133. 133.
    E. D. Lustbader and R. K. Stodola, “Algorithm AS 160. Partial and marginal association in multidimensional contingence tables,” Appl. Statist.,30, No. 1, 97–105 (1981).Google Scholar
  134. 134.
    R. R. Macdonald, “Algorithm AS 201. Combined significance test of differences between conditions and ordinal predictions,” Appl. Statist.,33, No. 2, 245–248 (1984).Google Scholar
  135. 135.
    G. MacKezie and M. O'Flaherty, “Algorithm AS 173. Direct design matrix generation for balanced factorial experiments,” Appl. Statist.,31, No. 1, 74–80 (1982).Google Scholar
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    K. L. Majunder and G. P. Bhattacharjee, “Algorithm AS 63. The incomplete beta integral,” Appl. Statist.,22, No. 3, 409–411 (1973).Google Scholar
  137. 137.
    K. L. Majunder and G. P. Bhattacharjee, “Algorithm AS 64. Inverse of the incomplete beta function ratio,” Appl. Statist.,22, No. 3, 411–414 (1973).Google Scholar
  138. 138.
    K. V. Mardia and P. J. Zemroch, “Algorithm AS 80. Spherical statistics,” Appl. Statist.,24, No. 1, 144–146 (1975).Google Scholar
  139. 139.
    K. V. Mardia and P. J. Zemroch, “Algorithm AS 81. Circular statistics,” Appl. Statist.,24, No. 1, 147–150 (1975).Google Scholar
  140. 140.
    K. V. Mardia and P. J. Zemroch, “Algorithm AS 84. Measures of multivariate skewness and kurtosis,” Appl. Statist.,24, No. 2, 262–265 (1975).Google Scholar
  141. 141.
    K. V. Mardia and P. J. Zemroch, “Algorithm AS 86. The von Mises distribution function,” Appl. Statist.,24, No. 2, 267–272 (1975).Google Scholar
  142. 142.
    N. W. A. Marsh, “Algorithm AS 227. Efficient generation of all binary patterns by Gray counting,” Appl. Statist.,36, No. 2, 245–249 (1987).Google Scholar
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  144. 144.
    N. S. Matloff, “Algorithm AS 148. The jacknife,” Appl. Statist.,29, No. 1, 115–117 (1980).Google Scholar
  145. 145.
    L. L. McDonald and H. R. Bauer, III, “Algorithm AS 161. Critical regions of an unconditional nonrandomized test of homogeneity in 2×2 contingency tables,” Appl. Statist.,30, No. 2, 182–189 (1981).Google Scholar
  146. 146.
    C. A. McGilchrist, “Algorithm AS 34. Sequential inversion of band matrices,” Appl. Statist.,19, No. 3, 290–292 (1970).Google Scholar
  147. 147.
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  149. 149.
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  150. 150.
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  180. 180.
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  184. 184.
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Copyright information

© Plenum Publishing Corporation 1990

Authors and Affiliations

  • G. V. Martynov

There are no affiliations available

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