Sequential Methods and Their Applications 1st Edition by Nitis Mukhopadhyay, Basil de Silva ISBN 1420010026 9781420010022

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Product details:

ISBN 10: 1420010026

ISBN 13: 9781420010022

Author:  Nitis Mukhopadhyay, Basil M. de Silva 

Sequential Methods and Their Applications 1st Edition: 

Interactively Run Simulations and Experiment with Real or Simulated Data to Make Sequential Analysis Come Alive

Taking an accessible, nonmathematical approach to this field, Sequential Methods and Their Applications illustrates the efficiency of sequential methodologies when dealing with contemporary statistical challenges in many areas.

The book first explores fixed sample size, sequential probability ratio, and nonparametric tests. It then presents numerous multistage estimation methods for fixed-width confidence interval as well as minimum and bounded risk problems. The book also describes multistage fixed-size confidence region methodologies, selection methodologies, and Bayesian estimation. Through diverse applications, each chapter provides valuable approaches for performing statistical experiments and facilitating real data analysis.

Functional in a variety of statistical problems, the authors’ interactive computer programs show how the methodologies discussed can be implemented in data analysis. Each chapter offers examples of input, output, and their interpretations. Available online, the programs provide the option to save some parts of an output so readers can revisit computer-generated data for further examination with exploratory data analysis.

Through this book and its computer programs, readers will better understand the methods of sequential analysis and be able to use them in real-world settings.

 

Sequential Methods and Their Applications 1st Edition Table of contents:

CHAPTER 1: Objectives, Coverage, and Hopes

CHAPTER 2: Why Sequential?

CHAPTER 3: Sequential Probability Ratio Test

CHAPTER 4: Sequential Tests for Composite Hypotheses

CHAPTER 5: Sequential Nonparametric Tests

CHAPTER 6: Estimation of the Mean of a Normal Population

CHAPTER 7: Location Estimation: Negative Exponential Distribution

CHAPTER 8: Point Estimation of Mean of an Exponential Population

CHAPTER 9: Fixed-Width Intervals from MLEs

CHAPTER 10: Distribution-Free Methods in Estimation

CHAPTER 11: Multivariate Normal Mean Vector Estimation

CHAPTER 12: Estimation in a Linear Model

CHAPTER 13: Estimating the Difference of Two Normal Means

CHAPTER 14: Selecting the Best Normal Population

CHAPTER 15: Sequential Bayesian Estimation

CHAPTER 16: Selected Applications

 

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