Statistical Inference for Discrete Time Stochastic Processes(Rajarshi)
Thu, 10 Jul 2014 06:01:06 GMT
series:SpringerBriefs in Statistics
This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN ...
Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications
Thu, 20 Jun 2013 23:02:34 GMT
series:Selected Papers of the Statistical Societies
This volume of the Selected Papers from Portugal is a product of the Seventeenth Congress of the Portuguese Statistical Society, held at the beautiful resort seaside city of Sesimbra, Portugal, from September 30 to October 3, 2009. It covers a broad scope of theoretical, methodological as well as application-oriented articles in domains such as: Linear Models and Regression, Survival Analysis, ...
Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications
Sat, 15 Jun 2013 23:03:30 GMT
series:Selected Papers of the Statistical Societies
This volume of the Selected Papers from Portugal is a product of the Seventeenth Congress of the Portuguese Statistical Society, held at the beautiful resort seaside city of Sesimbra, Portugal, from September 30 to October 3, 2009. It covers a broad scope of theoretical, methodological as well as application-oriented articles in domains such as: Linear Models and Regression, Survival Analysis, ...
Bayesian Networks in R(Nagarajan et al.)
Thu, 02 May 2013 00:17:17 GMT
with Applications in Systems Biology
series:Use R!
Bayesian Networks in R with Applications in Systems Biology introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is gradually increased across the chapters with exercises and solutions for enhanced understanding and hands-on experimentation of key ...
Selected Works of David Brillinger
Fri, 12 Apr 2013 23:19:19 GMT
series:Selected Works in Probability and Statistics
This volume contains 30 of David Brillinger's most influential papers. He is an eminent statistical scientist, having published broadly in time series and point process analysis, seismology, neurophysiology, and population biology. Each of these areas are well represented in the book. The volume has been divided into four parts, each with comments by one of Dr. Brillinger's former PhD ...
A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem(Desai)
Fri, 22 Mar 2013 13:40:51 GMT
with Simulations and Examples in SAS®
series:SpringerBriefs in Statistics
In statistics, the Behrens–Fisher problem is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples. In his 1935 paper, Fisher outlined an approach to the Behrens-Fisher problem. Since high-speed ...
A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem(Desai)
Fri, 01 Mar 2013 09:17:03 GMT
with Simulations and Examples in SAS®
series:SpringerBriefs in Statistics
In statistics, the Behrens–Fisher problem is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples. In his 1935 paper, Fisher outlined an approach to the Behrens-Fisher problem. Since high-speed ...
Essential Statistical Inference(Boos et al.)
Tue, 19 Feb 2013 06:12:38 GMT
Theory and Methods
series:Springer Texts in Statistics
This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and ...
Singular Spectrum Analysis for Time Series(Golyandina et al.)
Mon, 11 Feb 2013 09:09:59 GMT
series:SpringerBriefs in Statistics
Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on ...
Classification and Data Mining
Mon, 11 Feb 2013 08:47:06 GMT
series:Studies in Classification, Data Analysis, and Knowledge Organization
This volume contains both methodological papers showing new original methods, and papers on applications illustrating how new domain-specific knowledge can be made available from data by clever use of data analysis methods. The volume is subdivided in three parts: Classification and Data Analysis; Data Mining; and Applications. The selection of peer reviewed papers had been presented ...
Essentials of Monte Carlo Simulation(Thomopoulos)
Mon, 11 Feb 2013 08:24:33 GMT
Statistical Methods for Building Simulation Models
Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. ...
Smoothing Spline ANOVA Models(Gu)
Mon, 11 Feb 2013 07:40:46 GMT
series:Springer Series in Statistics
Nonparametric function estimation with stochastic data, otherwiseknown as smoothing, has been studied by several generations ofstatisticians. Assisted by the ample computing power in today'sservers, desktops, and laptops, smoothing methods have been findingtheir ways into everyday data analysis by practitioners. While scoresof methods have proved successful for univariate smoothing, ...
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis(Kjærulff et al.)
Mon, 11 Feb 2013 07:31:28 GMT
series:Information Science and Statistics
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. ...
Selected Works of Peter J. Bickel
Mon, 11 Feb 2013 07:31:27 GMT
series:Selected Works in Probability and Statistics
This volume presents selections of Peter J. Bickel’s major papers, along with comments on their novelty and impact on the subsequent development of statistics as a discipline. Each of the eight parts concerns a particular area of research and provides new commentary by experts in the area. The parts range from Rank-Based Nonparametrics to Function Estimation and Bootstrap Resampling. Peter’s ...
Linear Mixed-Effects Models Using R(Gałecki et al.)
Fri, 08 Feb 2013 08:12:44 GMT
A Step-by-Step Approach
series:Springer Texts in Statistics
Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing ...
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