Fundamentals of Mathematical Statistics: Statistical Inference - Brossura

Nguyen, Hung T.; Rogers, Gerald S.

Brossura

ISBN 10: 146138916X ISBN 13: 9781461389163

Casa editrice: Springer, 2011

Riassunto

This is a text (divided into two volumes) for a two semester course in Mathematical Statistics at the Senior/Graduate level. The two main pedagogical aspects in these Volumes are: (i) the material is designed in lessons (each for a 50 minute class) with complementary exercises and home work. (ii) although the material is traditional, great care is exerted upon self-contained, rigorous and complete presentations. An elementary introduction to characteristic functions and probability measures and intergration, but not general measure theory in Volume I, allows a complete proof of some central limit theorems and a rigorous treatment of asymptotic of statistical inference. But students need to be familiar only with such things as Jacobians and eigenvalues of matrices. Volume II: Statistical Inference is designed for the second semester and contains a rigorous introduction to Mathematical Statistics, from random samples to asymptotic theory of statistical inference.

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Recensione

"This two-volume text is designed for a two-semester course in mathematical statistics at the senior/graduate level... The authors have succeeded in presenting the material with clarity and enough details of analytical niceties." (Mathematical Reviews)

Contenuti

IV: Sampling and distributions.- Overview.- Lesson 1 Sampling and statistics.- 2 Transformations of real random variables.- 3 Transformations of random vectors.- 4 Sampling distributions in normal populations-I.- 5 Sampling distributions in normal populations-II.- 6 Order statistics.- 7 Sufficient statistics-I.- 8 Sufficient statistics-II.- 9 Complete statistics.- 10 Exponential families-I.- 11 Exponential families-II.- V: Statistical estimation.- Overview.- Lesson 1 Point estimation of parameters.- 2 Conditional expectation.- 3 Uniformly minimum variance unbiased estimators.- 4 Efficient estimators.- 5 Unbiased estimation: the vector case.- 6 Two methods of point estimation.- 7 Maximum likelihood estimation.- 8 Confidence interval estimation-I.- 9 Confidence interval estimation-II.- 10 Consistent estimators.- 11 Consistency of maximum likelihood estimators.- 12 Asymptotic normality.- 13 Asymptotic normality of maximum likelihood estimators.- 14 Asymptotic efficiency and large sample confidence intervals.- VI: Testing hypotheses.- Overview.- Lesson 1 Neyman-Pearson theory-I.- 2 Neyman-Pearson theory-II.- 3 Testing with monotone likelihood ratios.- 4 Testing when the support contains parameters.- 5 Unbiased tests.- 6 Quadratic forms in normal random variables.- 7 Likelihood ratio tests-I.- 8 One-way analysis of variance.- 9 Likelihood ratio tests-II.- 10 LRT-asymptotic distributions.- 11 Summary of tests for normal populations.- 12 Tests for two-by-two tables.- VII: Special topics.- Overview.- Lesson 1 Minimax and Bayes estimators-I.- 2 Minimax and Bayes estimators-II.- 3 Equivariant estimators.- 4 Simple linear regression-I.- 5 Simple linear regression-II.- 6 Sufficient statistics and uniformly most powerful tests.- 7 Sequential probability ratio tests.- 8 A test by Mann, Whitney, Wilcoxon.- 9 Tests for paired comparisons.- 10 Tests of Kolmogorov, Smirnov type.- 11 Categorical data.- Tables.- References.

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2011
• ISBN 10: 146138916X
• ISBN 13: 9781461389163
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 440