Mathematical statistics with applications in R / [electronic resource]

by Ramachandran, K. M [author.]; Tsokos, Chris P [author.].

Material type: materialTypeLabel

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.

Tags from this library: No tags from this library for this title. Add tag(s)

average rating: 0.0 (0 votes)

No physical items for this record

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.

Dhaka University Library Online

Mathematical statistics with applications in R / [electronic resource]

by Ramachandran, K. M [author.]; Tsokos, Chris P [author.].