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 students
Putting these pieces together, we have β1 − β2 ≥ 0, so the power for our likelihood ratio test in the Neyman–Pearson lemma is at least as large as the power for any other test with size α. Corollary to the Neyman–Pearson lemma: The ...
This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and ...
Example 1.17 How many toys to buy? Assuming that the number of children in the family follows a Poisson distribution with X = 2.4, how many toys should one buy so that every child gets a present.
This edition includes a plethora of new exercises, a number of which are similar to what would be encountered on the actuarial exams that cover probability and statistics.
Pearson, K. (1900). On a criterion that a given system of deviations from the probably in the case of a correlated system of variables is such that it can be reasonably be supposed to have arisen from random sampling.
The book was written for statistics students with one or two years of coursework in mathematical statistics and probability, professors who hold courses in mathematical statistics, and researchers in other fields who would like to do some ...
This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles.
This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation.
Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
In: International Encyclopedia of Statistical Science, Volumes I, II, III (ed. M. Lovric), 1361–1363. Berlin: Springer. Diggle, P. and Ribeiro, P. (2007). Model-Based Geostatistics. New York: Springer. Gaetan, C. and Guyon, H. (2010).