Mathematical Statistics with Applications in R

Mathematical Statistics with Applications in R

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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. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods Instructor's Manual; Solutions to Selected Problems, data sets, and image bank for studentsAndrei Kolmogorov (1903-1987) laid the mathematical foundations of probability theory and the theory of randomness. His monograph Grundbegriffe der Wahrscheinlichkeitsrechnung, published in 1933, introduced probability theory in aanbsp;...

Title:Mathematical Statistics with Applications in R
Author:K.M. Ramachandran, Chris P. Tsokos
Publisher:Elsevier - 2014-09-14


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