Mukhopadhyay | Introductory Statistical Inference | Buch | 978-1-138-06168-2 | www.sack.de

Buch, Englisch

Mukhopadhyay

Introductory Statistical Inference


1. Auflage 2017
ISBN: 978-1-138-06168-2
Verlag: CRC Press

Buch, Englisch

ISBN: 978-1-138-06168-2
Verlag: CRC Press


This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.

Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of distributions, and standard probability inequalities. It develops the Helmert transformation for normal distributions, introduces the notions of convergence, and spotlights the central limit theorems. Coverage highlights sampling distributions, Basu's theorem, Rao-Blackwellization and the Cramér-Rao inequality. The text also provides in-depth coverage of Lehmann-Scheffé theorems, focuses on tests of hypotheses, describes Bayesian methods and the Bayes' estimator, and develops large-sample inference. The author provides a historical context for statistics and statistical discoveries and answers to a majority of the end-of-chapter exercises.

Designed primarily for a one-semester, first-year graduate course in probability and statistical inference, this text serves readers from varied backgrounds, ranging from engineering, economics, agriculture, and bioscience to finance, financial mathematics, operations and information management, and psychology.

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Zielgruppe


Upper-level undergraduate and graduate students taking courses in probability and statistical inference.


Autoren/Hrsg.


Weitere Infos & Material


Probability and Distributions

Introduction
About Sets
Axiomatic Development of Probability
Conditional Probability and Independent Events
Discrete Random Variables

Continuous Random Variables

Some Useful Distributions

Exercises and Complements

Moments and Generating Functions

Introduction

Expectation and Variance

Moments and Moment Generating Function

Determination of a Distribution via MGF

Probability Generating Function

Exercises and Complements

Multivariate Random Variables

Introduction

Probability Distributions

Covariances and Correlation Coefficient

Independence of Random Variables

Bivariate Normal Distribution

Correlation Coefficient and Independence

Exponential Family

Selected Probability Inequalities

Exercises and Complements

Sampling Distribution

Introduction

Moment Generating Function Approach

Order Statistics

Transformation

Special Sampling Distributions

Multivariate Normal Distribution

Selected Reviews in Matrices

Exercises and Complements

Notions of Convergence

Introduction

Convergence in Probability

Convergence in Distribution

Convergence of Chi-Square, t, and F distributions

Exercises and Complements

Sufficiency, Completeness, and Ancillarity

Introduction

Sufficiency

Minimal Sufficiency

Information

Ancillarity

Completeness

Exercises and Complements

Point Estimation

Introduction

Maximum Likelihood Estimator

Criteria to Compare Estimators

Improved Unbiased Estimators via Sufficiency

Uniformly Minimum Variance Unbiased Estimator

Consistent Estimator

Exercises and Complements

Tests of Hypotheses

Introduction

Error Probabilities and Power Function

Simple Null vs. Simple Alternative

One-Sided Composite Alternative

Simple Null vs. Two-Sided Alternative

Exercises and Complements

Confidence Intervals

Introduction

One-Sample Problems

Two-Sample Problems

Exercises and Complements

Bayesian Methods

Introduction

Prior and Posterior Distributions

Conjugate Prior

Point Estimation

Examples with a Nonconjugate Prior

Exercises and Complements

Likelihood Ratio and Other Tests

Introduction

One-Sample LR Tests: Normal

Two-Sample LR Tests: Independent Normal

Bivariate Normal

Exercises and Complements

Large-Sample Methods

Introduction

Maximum Likelihood Estimation

Asymptotic Relative Efficiency

Confidence Intervals and Tests of Hypotheses

Variance Stabilizing Transformation

Exercises and Complements

Abbreviations, Historical Notes, and Tables

Abbreviations and Notations

Historical Notes

Selected Statistical Tables

References

Answers: Selected Exercises

Author Index

Subject Index



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