Mathematical statistics with applications in R / [electronic resource]
by Ramachandran, K. M [author.]; Tsokos, Chris P [author.].
Material type:
BookPublisher: London, UK : Academic Press, imprint of Elsevier, 2015.Edition: 2nd ed.Description: 1 online resource (xxiii, 800 pages).ISBN: 012417132X; 9780124171329.Subject(s): Mathematical statistics | Mathematical statistics -- Data processing | R (Computer program language) | Mathematical statistics | Mathematical statistics -- Data processing | R (Computer program language) | Statistics as Topic | Electronic booksOnline resources: ScienceDirect 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.
Includes bibliographical references and index.
Print version record.
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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