Computational Statistics [electronic resource] / by James E. Gentle.
By: Gentle, James E [author.].
Contributor(s): SpringerLink (Online service).
Material type: TextSeries: Statistics and Computing: Publisher: New York, NY : Springer New York, 2009Description: XXII, 728 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387981444.Subject(s): Distribution (Probability theory  Computer science  Mathematics  Computer science  Mathematical statistics  Electronic data processing  Data mining  Probability Theory and Stochastic Processes  Computational Mathematics and Numerical Analysis  Mathematics of Computing  Statistics and Computing/Statistics Programs  Numeric Computing  Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.2 Online resources: Click here to access onlineItem type  Current location  Call number  Status  Date due  Barcode  Item holds  

EBOOKS 
ISI Library, Kolkata

Available  EB1592 
Preliminaries  Mathematical and Statistical Preliminaries  Statistical Computing  Computer Storage and Arithmetic  Algorithms and Programming  Approximation of Functions and Numerical Quadrature  Numerical Linear Algebra  Solution of Nonlinear Equations and Optimization  Generation of Random Numbers  Methods of Computational Statistics  Graphical Methods in Computational Statistics  Tools for Identification of Structure in Data  Estimation of Functions  Monte Carlo Methods for Statistical Inference  Data Randomization, Partitioning, and Augmentation  Bootstrap Methods  Exploring Data Density and Relationships  Estimation of Probability Density Functions Using Parametric Models  Nonparametric Estimation of Probability Density Functions  Statistical Learning and Data Mining  Statistical Models of Dependencies.
Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationallyintensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationallyintensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra.
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