Advances in Ranking and Selection, Multiple Comparisons, and - download pdf or read online

By C. R. Rao (auth.), N. Balakrishnan, H. N. Nagaraja, N. Kannan (eds.)

ISBN-10: 0817632328

ISBN-13: 9780817632328

ISBN-10: 0817644229

ISBN-13: 9780817644222

S. Panchapakesan has made major contributions to rating and choice and has released in lots of different components of facts, together with order information, reliability idea, stochastic inequalities, and inference. Written in his honor, the twenty invited articles during this quantity mirror contemporary advances in those fields and shape a tribute to Panchapakesan’s impact and impression on those parts.

Thematically equipped, the chapters hide a extensive variety of issues from:

* Inference

* score and Selection

* a number of Comparisons and assessments

* contract overview

* Reliability

* Biostatistics

Featuring idea, tools, functions, and huge bibliographies with particular emphasis on fresh literature, this entire reference paintings will serve researchers, practitioners, and graduate scholars within the statistical and utilized arithmetic communities.

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Extra info for Advances in Ranking and Selection, Multiple Comparisons, and Reliability: Methodology and Applications

Example text

Gregory, A. W. and Veal, M. R. (1985). Formulating Wald tests of nonlinear restrictions, Econometrica, 53, 1465-1468. 18 C R, Rao 33. Hall, W. J. and Mathiason, D. J. (1990). On large sample estimation and testing in parameter models, International Statistical Review, 58, 77-97. 34. Harris, P. (1985). An asymptotic expansion for the null distribution of the efficient score statistics, Biometrika, 72, 653-659. 35. Harvey, A. (1990). The Econometric Analysis of Time Series, Philip Allan, Oxford, England.

White, H. (1982). Maximum likelihood estimation of misspecified models, Econometrica, 50, 1-25. 71. White, H. (1984). Asymptotic Theory for Econometricians, Academic Press, New York. EM Algorithm and Optimal Censoring Schemes for Progressively Type-II Censored Bivariate Normal Data N. -A. Kim Department of Mathematics and Statistics, McMaster University, Hamilton, Ontaiio, Canada Abstract: The EM algorithm is used to find the maximum Ukelihood estimates (MLEs) of the parameters of a bivariate normal distribution based on progressively Type-II right censored samples.

Combine (Xobsiyobs) and (Xcenii^cen) to form (X, Y) which is the complete data, where X = ( X l , XlJ and Y = ( F X , F J J . 1) ^m-i - m + 1). n < ^jk < + 0 C , ~ 0 C < J/jfc < + 0 0 . , fu and fc = 1,2,... mm) - 0 0 < IJjk < + 0 0 . :-"^"^-/^-v ^i^j g . ^ j ^ ^ ^ dard normal pdf and cdf, respectively. 7) with 0(-) and $(•) denoting the stan- The E M Algorithm The progressively Type-II right censored bivariate normal data can be viewed as an incomplete data and the EM algorithm can then be applied to determine the MLE of the parameter 9.

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Advances in Ranking and Selection, Multiple Comparisons, and Reliability: Methodology and Applications by C. R. Rao (auth.), N. Balakrishnan, H. N. Nagaraja, N. Kannan (eds.)

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