Advances in Statistical Decision Theory and Applications (eBook, PDF)

Redaktion: Panchapakesan, S.; Balakrishnan, N.

• Format: PDF

Produktbeschreibung

Shanti S. Gupta has made pioneering contributions to ranking and selection theory; in particular, to subset selection theory. His list of publications and the numerous citations his publications have received over the last forty years will amply testify to this fact. Besides ranking and selection, his interests include order statistics and reliability theory. The first editor's association with Shanti Gupta goes back to 1965 when he came to Purdue to do his Ph.D. He has the good fortune of being a student, a colleague and a long-standing collaborator of Shanti Gupta. The second editor's association with Shanti Gupta began in 1978 when he started his research in the area of order statistics. During the past twenty years, he has collaborated with Shanti Gupta on several publications. We both feel that our lives have been enriched by our association with him. He has indeed been a friend, philosopher and guide to us.

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Produktdetails

• Verlag: Birkhäuser Boston
• Seitenzahl: 498
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461223085
• Artikelnr.: 44194753

Autorenporträt

N. Balakrishnan is an Associate Director and Professor at Department of Aerospace Engineering and Supercomputer Edu- cation and Research Centre, Indian Institute of Science. His research interests include numerical electromagnetic, multi-parameter radars, and signal processing. His publications include 19 books and many peer-reviewed journal papers.

Inhaltsangabe

I: Bayesian Inference.- 1 Bayes for Beginners? Some Pedagogical Questions.- 2 Normal Means Revisited.- 3 Bayes m-Truncated Sampling Allocations for Selecting the Best Bernoulli Population.- 4 On Hierarchical Bayesian Estimation and Selection for Multivariate Hypergeometric Distributions.- 5 Convergence Rates of Empirical Bayes Estimation and Selection for Exponential Populations With Location Parameters.- 6 Empirical Bayes Rules for Selecting the Best Uniform Populations.- II: Decision Theory.- 7 Adaptive Multiple Decision Procedures for Exponential Families.- 8 Non-Informative Priors Via Sieves and Packing Numbers.- III: Point And Interval Estimation-Classical Approach.- 9 From Neyman's Frequentism to the Frequency Validity in the Conditional Inference.- 10 Asymptotic Theory for the Simex Estimator in Measurement Error Models.- 11 A Change Point Problem for Some Conditional Functionals.- 12 On Bias Reduction Methods in Nonparametric Regression Estimation.- 13 Multiple Comparisons With the Mean.- IV: Tests Of Hypotheses.- 14 Properties of Unified Bayesian-Frequentist Tests.- 15 Likelihood Ratio Tests and Intersection-Union Tests.- 16 The Large Deviation Principle for Common Statistical Tests Against a Contaminated Normal.- 17 Multiple Decision Procedures for Testing Homogeneity of Normal Means With Unequal Unknown Variances.- V: Ranking and Selection.- 18 A Sequential Multinomial Selection Procedure With Elimination.- 19 An Integrated Formulation for Selecting the Best From Several Normal Populations in Terms of the Absolute Values of Their Means: Common Known Variance Case.- 20 Applications of Two Majorization Inequalities to Ranking and Selection Problems.- VI: Distributions AND Applications.- 21 Correlation Analysis of Ordered Observations From aBlock-Equicorrelated Multivariate Normal Distribution.- 22 On Distributions With Periodic Failure Rate and Related Inference Problems.- 23 Venn Diagrams, Coupon Collections, Bingo Games and Dirichlet Distributions.- VII: Industrial Applications.- 24 Control Charts for Autocorrelated Process Data.- 25 Reconstructive Estimation in a Parametric Random Censorship Model With Incomplete Data.- 26 A Review of the Gupta-Sobel Subset Selection Rule for Binomial Populations With Industrial Applications.- 27 The Use of Subset Selection in Combined-Array Experiments to Determine Optimal Product or Process Designs.- 28 Large-Sample Approximations to Best Linear Unbiased Estimation and Best Linear Unbiased Prediction Based on Progressively Censored Samples and Some Applications.