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      Book

      Mathematical Statistics
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      Book

      Mathematical Statistics

      DOI link for Mathematical Statistics

      Mathematical Statistics book

      Mathematical Statistics

      DOI link for Mathematical Statistics

      Mathematical Statistics book

      ByKeith Knight
      Edition 1st Edition
      First Published 1999
      eBook Published 24 November 1999
      Pub. Location New York
      Imprint Chapman and Hall/CRC
      DOI https://doi.org/10.1201/9780367805319
      Pages 504
      eBook ISBN 9780367805319
      Subjects Mathematics & Statistics
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      Knight, K. (1999). Mathematical Statistics (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780367805319

      ABSTRACT

      Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to understand the theoretical underpinnings of statistical methodology.

      The author concentrates on inferential procedures within the framework of parametric models, but - acknowledging that models are often incorrectly specified - he also views estimation from a non-parametric perspective. Overall, Mathematical Statistics places greater emphasis on frequentist methodology than on Bayesian, but claims no particular superiority for that approach. It does emphasize, however, the utility of statistical and mathematical software packages, and includes several sections addressing computational issues.

      The result reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry.



      Features



    • Provides the tools that allow an understanding of the underpinnings of statistical methods


    • Encourages the use of statistical software, which widens the range of problems reader can consider


    • Brings relevance to the subject-shows readers it has much to offer beyond optimality theory


    • Focuses on inferential procedures within the framework of parametric models, but also views estimation from the nonparametric perspective


    • Solutions manual availalbe on crcpress.com


    •  

      TABLE OF CONTENTS
      INTRODUCTION TO PROBABILITY
      RANDOM VECTORS AND JOINT DISTRIBUTIONS
      CONVERGENCE OF RANDOM VARIABLES
      PRINCIPLES OF POINT ESTIMATION
      LIKELIHOOD-BASED ESTIMATION
      OPTIMAL ESTIMATION
      INTERVAL ESTIMATION AND HYPOTHESIS TESTING
      LINEAR AND GENERALIZED LINEAR MODELS
      GOODNESS OF FIT
      REFERENCES



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