Posted by **interes** at Jan. 4, 2015

English | 2013-02-06 | ISBN: 1461448174 | PDF | 587 pages | 5,5 MB

Posted by **Specialselection** at Jan. 11, 2014

English | 2013-02-06 | ISBN: 1461448174 | 566 pages | PDF | 5.5 mb

Posted by **ChrisRedfield** at Aug. 3, 2013

Published: 2013-02-06 | ISBN: 1461448174 | PDF | 587 pages | 5 MB

Posted by **fdts** at March 31, 2015

by James H. Stapleton

English | 2007 | ISBN: 0470073721 | 464 pages | PDF | 28.95 MB

Posted by **nebulae** at Nov. 26, 2016

English | ISBN: 0759123403, 0759123411 | 2013 | 255 pages | PDF | 5 MB

Posted by **tanas.olesya** at Sept. 21, 2016

English | 24 May 2006 | ISBN: 0387249753 | 688 Pages | PDF | 3 MB

Optimization Theory and Methods can be used as a textbook for an optimization course for graduates and senior undergraduates.

Posted by **fdts** at Sept. 14, 2016

by Denis Bosq (Author), Hung T. Nguyen

English | 1996 | ISBN: 9048147131 | 354 pages | PDF | 6.23 MB

Posted by **Nice_smile)** at Sept. 23, 2015

English | Mar. 29, 1991 | ISBN: 0824785061 | 512 Pages | PDF | 15.06 MB

Textbook for a methods course or reference for an experimenter who is mainly interested in data analyses rather than in the mathematical development of the procedures.

Posted by **fdts** at Oct. 27, 2014

by Marcello D'Orazio, Marco Di Zio, Mauro Scanu

English | 2006 | ISBN: 0470023538 | 268 pages | PDF | 3.6 MB

Posted by **AlenMiler** at Aug. 19, 2014

November 25, 2013 | ISBN: 3642378862 | Pages: 376 | PDF | 8 MB

This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective.

A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.