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Mathematical statistics with applications in R / by Kandethody M. Ramachandran, Chris P. Tsokos.

By: Contributor(s): Material type: TextTextPublisher: London, UK : Academic Press, imprint of Elsevier, 2015Copyright date: ©2015Edition: Second editionDescription: xxiii, 800 pagesContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780124171329
Subject(s): Genre/Form: Additional physical formats: Print version:: No titleDDC classification:
  • 519.5 23 RAM
LOC classification:
  • QA276 RAM
Contents:
Statistical estimation,Hypothesis testing,sampling distribution
Summary: Mathematical Statistics with Applications, Second Edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces many modern statistical computational and simulation concepts that are not covered in other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Engineering students, especially, will find these methods to be very important in their studies.
Item type: Books
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode
Books Books GSU Library Epoch General Stacks Non-fiction QA276RAM (Browse shelf(Opens below)) 1 Available 50000001648
Books Books GSU Library Epoch General Stacks Non-fiction QA276RAM (Browse shelf(Opens below)) 2 Available 50000005240

Includes bibliographical references and index.

Statistical estimation,Hypothesis testing,sampling distribution

Mathematical Statistics with Applications, Second Edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces many modern statistical computational and simulation concepts that are not covered in other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Engineering students, especially, will find these methods to be very important in their studies.

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