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1-5 of 5 results for Author: dipak k dey (sorted by Publication Date, showing all)
  1. Extreme Value Modeling and Risk Analysis

    Methods and Applications

    Edited by Dipak K. Dey, Jun Yan

    To Be Published July 25th 2015 by Chapman and Hall/CRC

  2. Current Trends in Bayesian Methodology with Applications

    Edited by Satyanshu Kumar Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan

    This book provides a comprehensive survey in one place of recent results in these areas: Novel Bayesian Modeling; Spatio-temporal modeling; Data mining and machine learning; Bayesian non-parametric methods; Neuroimaging and Image analysis; Meta analysis; Survival analysis; Bayesian econometrics;...

    To Be Published May 14th 2015 by Chapman and Hall/CRC

  3. Bayesian Modeling in Bioinformatics

    Edited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick

    Series: Chapman & Hall/CRC Biostatistics Series

    Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad...

    Published September 2nd 2010 by Chapman and Hall/CRC

  4. A First Course in Linear Model Theory

    By Nalini Ravishanker, Dipak K. Dey

    Series: Chapman & Hall/CRC Texts in Statistical Science

    This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the mathematical and...

    Published December 20th 2001 by Chapman and Hall/CRC

  5. Generalized Linear Models

    A Bayesian Perspective

    Edited by Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick

    Series: Chapman & Hall/CRC Biostatistics Series

    This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references...

    Published May 24th 2000 by CRC Press

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