Posted by **Underaglassmoon** at April 15, 2016

Springer | Database Management | March 24 2016 | ISBN-10: 1447167910 | 647 pages | pdf | 7.5 mb

Authors: Lerman, Israël César

Presents brand new principles and methods within the Data Mining field

Examines ascendant agglomerative hierarchical clustering and Likelihood Linkage Analysis (LLA) clustering methods from metrical, algorithmic and computational aspects

Posted by **arundhati** at July 22, 2015

2015 | ISBN-10: 1466554924 | 220 pages | PDF | 4 MB

Posted by **BUGSY** at June 4, 2015

English | July 13, 2006 | ISBN: 1584886315 | 411 Pages | PDF | 6 MB

Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range…

Posted by **BUGSY** at May 19, 2015

English | July 25, 2003 | ISBN: 0471360910 | 739 Pages | PDF | 12 MB

Perfected over three editions and more than forty years, this field- and classroom-tested reference:

* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.

Posted by **tanas.olesya** at Feb. 15, 2015

English | Mar 27, 2008 | ISBN: 1584886307 | 207 Pages | PDF | 1 MB

Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies.

Posted by **tanas.olesya** at Dec. 7, 2014

English | July 30, 1999 | ISBN: 0387987754 | 305 pages | PDF | 1 MB

Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.

Posted by **tanas.olesya** at Nov. 27, 2014

English | March 22, 2007 | ISBN: 0387954406 | 758 pages | PDF | 13 MB

This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary.

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.

Posted by **Veslefrikk** at Aug. 17, 2013

Chapman & Hall/CRC | 2006 | ISBN: 1584886315 | 416 pages | PDF| 5,4 MB

Posted by **arundhati** at Dec. 10, 2016

2016 | ISBN-10: 1498700500 | 208 pages | PDF | 41 MB