Sequential Estimation (Wiley Series in Probability and Statistics) - Hardcover

Über die Autorin bzw. den Autor

MALAY GHOSH, PhD, is a professor in the University of FloridaDepartment of Statistics.

NITIS MUKHOPADHYAY, PhD, is a professor in the University ofConnecticut Department of Statistics.

PRANAB K. SEN, PhD, is a professor in the Department ofBiostatistics and Statistics of the University of North Carolina.

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The only comprehensive guide to the theory and practice of one of today's most important probabilistic techniques

The past 15 years have witnessed many significant advances in sequential estimation, especially in the areas of three-stage and nonparametric methodology. Yet, until now, there were no references devoted exclusively to this rapidly growing statistical field.

Sequential Estimation is the first, single-source guide to the theory and practice of both classical and modern sequential estimation techniques―including parametric and nonparametric methods. Researchers in sequential analysis will appreciate the unified, logically integrated treatment of the subject, as well as coverage of important contemporary procedures not covered in more general sequential analysis texts, such as:

• Shrinkage estimation
• Empirical and hierarchical Bayes procedures
• Multistage sampling and accelerated sampling procedures
• Time-sequential estimation
• Sequential estimation in finite population sampling
• Reliability estimation and capture-recapture methodologies leading to sequential tagging schemes

An indispensable resource for researchers in sequential analysis, Sequential Estimation is an ideal graduate-level text as well.

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The only comprehensive guide to the theory and practice of one of today's most important probabilistic techniques

The past 15 years have witnessed many significant advances in sequential estimation, especially in the areas of three-stage and nonparametric methodology. Yet, until now, there were no references devoted exclusively to this rapidly growing statistical field.

Sequential Estimation is the first, single-source guide to the theory and practice of both classical and modern sequential estimation techniques—including parametric and nonparametric methods. Researchers in sequential analysis will appreciate the unified, logically integrated treatment of the subject, as well as coverage of important contemporary procedures not covered in more general sequential analysis texts, such as:

• Shrinkage estimation
• Empirical and hierarchical Bayes procedures
• Multistage sampling and accelerated sampling procedures
• Time-sequential estimation
• Sequential estimation in finite population sampling
• Reliability estimation and capture-recapture methodologies leading to sequential tagging schemes

An indispensable resource for researchers in sequential analysis, Sequential Estimation is an ideal graduate-level text as well.