Sequential Methods and Their Applications

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A01=Basil M. de Silva
A01=Nitis Mukhopadhyay
Alternative Hypothesis H1
ASN Function
Author_Basil M. de Silva
Author_Nitis Mukhopadhyay
Bayesian data analysis
Bounded Risk Point Estimation
Category=PBT
Category=PS
confidence
Confidence Coefficient
Confidence Interval
confidence interval methods
Confidence Region
Coverage Probability
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
experimental design statistics
fixed
Fixed Width Confidence Interval
Fixed Width Confidence Interval Procedure
Fixed Width Interval
Independent Observations X1
initial
Initial Sample Size
interval
methodology
Minimum Risk Point Estimation
multistage estimation
Negative Exponential Distribution
nonparametric inference
optimal
Optimal Fixed Sample Size
Optimal Sample Size
Program Number
random
sample
sequential analysis for real data applications
Sequential Bayesian Estimation
Sequential Methodology
Sequential Procedure
Sequential Strategy
Simple Null Hypothesis H0
size
SPRT
statistical hypothesis testing
Test H0
UMVUE
width

Product details

  • ISBN 9780367386535
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Mukhopadhyay, Nitis; de Silva, Basil M.