Statistical computation

proceedings of a conference held at the University of Wisconsin, Madison, Wisconsin, April 28-20 1969
  • 462 Pages
  • 4.26 MB
  • 4062 Downloads
  • English
by
Academic Press , New York, London
Electronic data processing -- Mathematical statistics., Electronic data processing -- Statis
Statementedited by Roy C. Milton, John A. Nelder.
ContributionsMilton, Roy C., Nelder, J. A.
Classifications
LC ClassificationsQA276.4
The Physical Object
Paginationxii,462p. :
ID Numbers
Open LibraryOL14939093M
ISBN 10012498150X

The book focuses on the methodologies, techniques, principles, and approaches involved in statistical computation. The selection first elaborates on the description of data structures for statistical computing, autocodes for the statistician, and an experimental data structure for statistical computing.

Details Statistical computation EPUB

An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods.

It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques. The book focuses on the methodologies, techniques, principles, and approaches involved in statistical computation.

The selection first elaborates on the description of data structures for statistical computing, autocodes for the statistician, and an Book Edition: 1. Published online: 9 Jan Hidden Markov model in multiple testing on dependent count data.

Published online: 7 Jan See all volumes and issues. Vol Vol This book is another text that covers statistical methods using R.

Description Statistical computation PDF

Many of the others are specialized as for example Mark Chang's which covers adaptive designs only and Jim Albert's deals only with Bayesian methods. This is the best that I have seen to cover advanced statistical topics and teach the essentials of R for statistical by: [This] book is outstanding in terms of coverage of topics, rigorous language and integration of computation.” (Abdolvahab Khademi, Journal of Statistical Software, August ) From the Back Cover Statistical computation book textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate by: Computational statistics, or statistical computing, is the interface between statistics and computer is the area of computational science (or scientific computing) specific to the mathematical science of area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education.

Welcome to the VassarStats website, which I hope you will find to be a useful and user-friendly tool for performing statistical computation. Each of the links in white text in the panel on the left will show an annotated list of the statistical procedures available under that rubric.

The «Site Map» display below will show a complete list of. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting.

The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte.

Statistical computation. [J H Maindonald] Describes the computing techniques used to solve statistical problems. Indentifies the major themes, ideas, and methods for describing algorithms. Book\/a>, schema:CreativeWork\/a> ; \u00A0\u00A0\u00A0\n library.

Avi Wigderson Mathematics and Computation Draft: Ma Acknowledgments In this book I tried to present some of the knowledge and understanding I acquired in my four decades in the eld. The main source of this knowledge was the Theory of Computation commu-nity, which has been my academic and social home throughout this period.

Statistical Modeling and Computation offers a singular introduction to trendy Statistics from each classical and Bayesian views. It additionally gives an built-in remedy of Mathematical Statistics and trendy statistical computation, emphasizing statistical modeling, computational methods, and purposes.

Statistical Computation The SURVEYMEANS procedure uses the Taylor expansion method to estimate sampling errors of estimators based on complex sample designs. This method obtains a linear approximation for the estimator and then uses the variance estimate for this approximation to estimate the variance of the estimate itself (Woodruff Volume 23 Volume 20 Volume 16 Volume 14 Volume 12   “This book provides the researcher with an overview of the issues involved in the implementation and computation of common statistical procedures.” (Statistical Methods in Medical Research, Vol.

14, ) " this book is a good reference for social scientists that are involved in computational statistics.". "The publication of this book, I believe, is a milestoneKennedy and Gentle have done an outstanding job of assembling the best techniques from a great variety of sources, establishing a benchmark for the field of statistical computing.

"Mathematics of Computation. Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java.

GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up Solutions for selected exercises from the book "Statistical Computing with R" by Maria L. Rizzo.

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This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences.

Title of a book, article or other published item (this will display to the public): VassarStats: Statistical Computation Web Site. What type of media is this winner?: Website. Winner Detail Create Date: Thursday, Septem - Winner Detail Change Date. 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. Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical.

In this convenient textbook and reference work, the reader will find an introduction to statistical computing and a critical, balanced presentation of the algorithms and computational methods currently in use.

Emphasizing the most accurate and widely used of these methods, the book thoroughly describes the algorithms that have been incorporated into the leading software systems of today, and 4/5(1).

Statistical Modeling and Computation This homepage accompanies the book: D.P. Kroese and J.C.C. Chan. Statistical Modeling and Computation, Springer, [ Springer | Amazon ] The table of contents and Part I of the book can be downloaded for free here. Statistics and computing share many close relationships.

Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science.

Elements of Statistical Computing covers the broad usage of computing in statistics. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications.

book is outstanding in. Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied.

Populations can be diverse groups of people or objects such as "all people living in a country" or "every. Statistical Modeling and Computation - Ebook written by Dirk P. Kroese, Joshua C.C. Chan. Read this book using Google Play Books app on your PC, android, iOS devices.

Download for offline reading, highlight, bookmark or take notes while you read Statistical Modeling and Computation.

allows statistical analysis and visualisation of unlimited sophisti-cation; you are not restricted to a small set of procedures or options, and because of the contributed packages, you are not limited to one method of accomplishing a given computation or graphical presen-tation. Statistical Analysis Handbook A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools (such as design of experiments and computation of life insurance premiums) to almost every walk of life.

is the primary function of modern statistical methods. Our objective in producing this Handbook is to be comprehensive in File Size: 1MB. Statistical Modeling and Computation Article (PDF Available) in Journal of statistical software 66(Book Review 3) August with Reads How we measure 'reads'.Welcome to «Concepts and Applications of Inferential Statistics», which is a free, full-length, and occasionally interactive statistics textbook.

It is a companion site of «VassarStats: Web Site for Statistical Computation». The materials on this site may be freely used .This book provides an introduction to statistical computing and a critical, balanced presentation of the algorithms and computational methods used in software systems, discussing techniques for implementing algorithms in a computer.

It is intended for graduate students in statistics.