Introductory Statistical Inference

AvNitis Mukhopadhyay

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PDF, Engelska, 2006

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Beskrivning

Introductory Statistical Inference develops the concepts and intricacies of statistical inference. With a review of probability concepts, this book discusses topics such as sufficiency, ancillarity, point estimation, minimum variance estimation, confidence intervals, multiple comparisons, and large-sample inference. It introduces techniques of two-stage sampling, fitting a straight line to data, tests of hypotheses, nonparametric methods, and the bootstrap method. It also features worked examples of statistical principles as well as exercises with hints. This text is suited for courses in probability and statistical inference at the upper-level undergraduate and graduate levels.

Produktinformation

Utgivningsdatum:2006-02-07
Språk:Engelska
Filformat:PDF
Kopieringsskydd:LCP
ISBN:9781420017403
Förlag:Taylor & Francis Ltd

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Recensioner i media

"The style and contents of the book are well organized, which makes it easy to read…recommended as a one- or two-semester course book and also as a supplementary textbook for different advanced statistical courses."-Fazil A. Aliev, Mathematical Reviews, Issue 2007a"…Professor Mukhopadhyay has done an excellent job of collecting a broad statistical knowledge base on the various topics covered and presented them in a straightforward manner … Not many books on statistical inference with such features are available currently."-- Sunil K. Dhar, New Jersey Institute of Technology"This book is designed for a one-semester course in Mathematical Statistics. In many universities, graduate students from Economics, Actuarial Sciences, Finance, and several other departments do not have enough time to take a two-semester sequence … For them, this book is ideal. They can learn wide varieties of topics in Mathematical Statistics in one course."-- Dipak K. Dey, University of Connecticut

Innehållsförteckning

Review of Probability and Related Concepts. Sufficiency, Completeness, and Ancillarity. Point Estimation. Tests of Hypotheses. Confidence Interval Estimation. Bayesian Methods. Likelihood Ratio and Other Tests. Large-Sample Inference. Sample Size Determination: Two-Stage Procedures. Regression Analysis: Fitting a Straight Line. Nonparametric Methods. Bootstrap Methods. Appendix. References.