Introduction to Statistical Limit Theory

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A01=Alan M. Polansky
advanced statistical exercises
Alternative Hypothesis H1
Asymptotic Expansion
Asymptotic Expansions
Asymptotic Power
Asymptotic Relative Efficiency
Asymptotic Theory
asymptotic theory applications in research
Author_Alan M. Polansky
Bayesian Estimation
Category=PBT
Central Limit Theorem
Characteristic Function
Confidence Intervals
convergence analysis
Convergence Of Moments
Cornish Fisher Expansion
Distribution Function
Edgeworth Expansion
Empirical Distribution Function
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Finite Variance ?2
Finite Variance Σ2
graduate level statistics
Hypothesis Tests
Independent Discrete Random Variables
Independent Random Variables
Limit Random Variable
Linear Rank Statistic
mathematical statistics
Maximum Likelihood Estimator
Modes Of Convergence
Null Hypothesis
Null Hypothesis H0
Pitman Asymptotic Relative Efficiency
Pointwise Bias
probability theory
Random Variables
Random Vectors
Sample Central Moments
statistical inference methods
Statistical Limit Theory
Test Statistic Tn
Truncated Random Variables
Uniformly Integrable

Product details

  • ISBN 9781420076608
  • Weight: 1020g
  • Dimensions: 156 x 234mm
  • Publication Date: 07 Jan 2011
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Helping students develop a good understanding of asymptotic theory, Introduction to Statistical Limit Theory provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. It also discusses how the results can be applied to several common areas in the field.

The author explains as much of the background material as possible and offers a comprehensive account of the modes of convergence of random variables, distributions, and moments, establishing a firm foundation for the applications that appear later in the book. The text includes detailed proofs that follow a logical progression of the central inferences of each result. It also presents in-depth explanations of the results and identifies important tools and techniques. Through numerous illustrative examples, the book shows how asymptotic theory offers deep insight into statistical problems, such as confidence intervals, hypothesis tests, and estimation.

With an array of exercises and experiments in each chapter, this classroom-tested book gives students the mathematical foundation needed to understand asymptotic theory. It covers the necessary introductory material as well as modern statistical applications, exploring how the underlying mathematical and statistical theories work together.

Alan M. Polansky is an associate professor in the Division of Statistics at Northern Illinois University. Dr. Polansky is the author of Observed Confidence Levels: Theory and Application (CRC Press, October 2007). His research interests encompass nonparametric statistics and industrial applications of statistics.

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