Cassel, C.M., Särndal, C.E. and Wretman, J.H. (1977). Foundation of Inference in Survey Sampling. Wiley, New York.
MATH
Google Scholar
Chen, J., Chen, S.Y. and Rao, J.N.K. (1998). Empirical likelihood confidence intervals for the mean of a population containing many zero values. The Canadian Journal of Statistics 31, 1, 53–68.
MathSciNet
Article
MATH
Google Scholar
Cox, D.R. and Snell, E.J. (1979). On sampling and the estimation of rare errors. Biometrika 66, 1, 125–132. Errata: Biometrika 69(2), 491 (1982).
MathSciNet
Article
MATH
Google Scholar
Cramér, H. (1962). Mathematical Methods of Statistics. Asia Publishing House, Bombay.
MATH
Google Scholar
Dimitrov, B., Green, D. Jr., Rykov, V. and Stanchev, P. (2003). On statistical hypothesis testing via simulation method. International Journal of Information Theories and Applications 10, 408–414.
Google Scholar
Davison, A.C. and Hinkley, D.V. (1997). Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge.
Book
MATH
Google Scholar
Dufour, J.M. (2006). Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics. Journal of Econometrics 133, 443–477.
MathSciNet
Article
MATH
Google Scholar
Dufour, J.M. and Khalaf, L. (2001). Monte Carlo test methods in econometrics, Oxford, Baltagi, B. (ed.), p. 494–519.
Fienberg, S.E., Nether, J. and Leitch, R.A. (1977). Estimating the total overstatement error in accounting populations. Journal of the American Statistical Association 72, 295–302.
Article
Google Scholar
Frost, P.A. and Tamura, H. (1986). Accuracy of auxiliary information interval estimation in statistical auditing. Journal of Accounting Research 24, 57–75.
Article
Google Scholar
Ghosh, M. and Meeden, G. (1997). Bayesian Methods for Finite Population Sampling. Chapman & Hall, London.
Book
MATH
Google Scholar
Guy, D.M. and Carmichael, D.R. (1986). Audit sampling: An introduction to statistical sampling in auditing. Wiley, New York.
Google Scholar
Hall, P. (1992). The Bootstrap and Edgewrth Expansion. Springer-Verlag, New York.
Book
Google Scholar
Kvanli, A.H., Shen, Y.K. and Deng, L.Y. (1998). Construction of confidence intervals for the mean of a population containing many zero values. Journal of Business and Economic Statistics 16, 362–368.
Google Scholar
MacKinnon, J. (2007). Bootstrap hypothesis testing. Queen’s Economics Department, Working Paper no 1127.
Marazzi, A. and Tillé, Y. (2016). Using past experience to optimize audit sampling design. Review of Quantive Finance Accounting 1-28, https://doi.org/10.1007/s11156-016-0596-7.
McLachlan, G. and Peel, D. (2000). Finite Mixture Models. Wiley, New York.
Book
MATH
Google Scholar
Meng, X.L. (1977). The EM algorithm and medical studies: A historical link. Statistical Research Methods in Medical Research 6, 3–23.
Article
Google Scholar
Silvey, S.D. (1959). The Lagrangian multiplier test. The Annals of Mathematical Statistics 30, 2, 389–407.
MathSciNet
Article
MATH
Google Scholar
Särndal, C.-E., Swensson, B. and Wretman, J. (1989). Statistical models and analysis in auditing. Panel on nonstandard mixtures of distributions. Statistical Science
4, 1, 2–33.
Article
Google Scholar
Tamura, H. (1988). Estimation of rare errors using judgement. Biometrika 75, 1–9.
MathSciNet
Article
MATH
Google Scholar
Wywiał, J.L. (2016). Contributions to Testing Statistical Hypotheses in Auditing. PWN, Warsaw.
Google Scholar