Linear Statistical Inference and its Applications

Released on by C. Radhakrishna Rao
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  • Linear Statistical Inference and its Applications Book Detail

  • Author : C. Radhakrishna Rao
  • Release Date : 2009-09-25
  • Publisher : John Wiley & Sons
  • Genre : Mathematics
  • Pages : 656
  • ISBN 13 : 0470317140
  • File Size : 43,43 MB
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Linear Statistical Inference and its Applications by C. Radhakrishna Rao PDF Summary

Book Description: "C. R. Rao would be found in almost any statistician's list of five outstanding workers in the world of Mathematical Statistics today. His book represents a comprehensive account of the main body of results that comprise modern statistical theory." -W. G. Cochran "[C. R. Rao is] one of the pioneers who laid the foundations of statistics which grew from ad hoc origins into a firmly grounded mathematical science." -B. Efrom Translated into six major languages of the world, C. R. Rao's Linear Statistical Inference and Its Applications is one of the foremost works in statistical inference in the literature. Incorporating the important developments in the subject that have taken place in the last three decades, this paperback reprint of his classic work on statistical inference remains highly applicable to statistical analysis. Presenting the theory and techniques of statistical inference in a logically integrated and practical form, it covers: * The algebra of vectors and matrices * Probability theory, tools, and techniques * Continuous probability models * The theory of least squares and the analysis of variance * Criteria and methods of estimation * Large sample theory and methods * The theory of statistical inference * Multivariate normal distribution Written for the student and professional with a basic knowledge of statistics, this practical paperback edition gives this industry standard new life as a key resource for practicing statisticians and statisticians-in-training.

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Information Theory and Statistics

Information Theory and Statistics

File Size : 64,64 MB
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Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and pr