Computational Methods in Statistics and Econometrics

Hisashi Tanizaki

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January 21, 2004 by CRC Press
Textbook - 528 Pages
ISBN 9780203022023 - CAT# KE00076
Series: Statistics: A Series of Textbooks and Monographs

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  • Offers a comprehensive review of introductory notions in statistics such as variable transformation, statistical inference, hypothesis testing, and regression analysis
  • Illuminates the fundamental topic of uniform random number generation
  • Demonstrates the generation of diverse continuous- and discrete-type random draws
  • Explains the inverse transform method for use with Laplace, Cauchy, and other distributions
  • Details random draw approaches for multivariate distributions
  • Describes random number generation via importance and rejection sampling methods and the Metropolis-Hastings algorithm
  • Outlines nonparametric methods for finding the difference between two sample means, including logistic and Cauchy score tests and Fisher's randomization test
  • Considers Monte Carlo and nonparametric methods for testing correlation and regression coefficients and establishing the independence of two samples
  • Summary

    Reflecting current technological capacities and analytical trends, Computational Methods in Statistics and Econometrics showcases Monte Carlo and nonparametric statistical methods for models, simulations, analyses, and interpretations of statistical and econometric data. The author explores applications of Monte Carlo methods in Bayesian estimation, state space modeling, and bias correction of ordinary least squares in autoregressive models. The book offers straightforward explanations of mathematical concepts, hundreds of figures and tables, and a range of empirical examples. A CD-ROM packaged with the book contains all of the source codes used in the text.


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