Scientific Inference, Data Analysis, and Robustness PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Scientific Inference, Data Analysis, and Robustness PDF full book. Access full book title Scientific Inference, Data Analysis, and Robustness by G. E. P. Box. Download full books in PDF and EPUB format.
Author: G. E. P. Box
Publisher: Academic Press
ISBN: 1483259390
Category : Mathematics
Languages : en
Pages : 317
Get Book
Book Description
Mathematics Research Center Symposium: Scientific Inference, Data Analysis, and Robustness focuses on the philosophy of statistical modeling, including model robust inference and analysis of data sets. The selection first elaborates on pivotal inference and the conditional view of robustness and some philosophies of inference and modeling, including ideas on modeling, significance testing, and scientific discovery. The book then ponders on parametric empirical Bayes confidence intervals, ecumenism in statistics, and frequency properties of Bayes rules. Discussions focus on consistency of Bayes rules, scientific method and the human brain, and statistical estimation and criticism. The book takes a look at the purposes and limitations of data analysis, likelihood, shape, and adaptive inference, statistical inference and measurement of entropy, and the robustness of a hierarchical model for multinomials and contingency tables. Topics include numerical results for contingency tables and robustness, multinomials, flattening constants, and mixed Dirichlet priors, entropy and likelihood, and test as measurement of entropy. The selection is a valuable reference for researchers interested in robust inference and analysis of data sets.
Author: G. E. P. Box
Publisher: Academic Press
ISBN: 1483259390
Category : Mathematics
Languages : en
Pages : 317
Get Book
Book Description
Mathematics Research Center Symposium: Scientific Inference, Data Analysis, and Robustness focuses on the philosophy of statistical modeling, including model robust inference and analysis of data sets. The selection first elaborates on pivotal inference and the conditional view of robustness and some philosophies of inference and modeling, including ideas on modeling, significance testing, and scientific discovery. The book then ponders on parametric empirical Bayes confidence intervals, ecumenism in statistics, and frequency properties of Bayes rules. Discussions focus on consistency of Bayes rules, scientific method and the human brain, and statistical estimation and criticism. The book takes a look at the purposes and limitations of data analysis, likelihood, shape, and adaptive inference, statistical inference and measurement of entropy, and the robustness of a hierarchical model for multinomials and contingency tables. Topics include numerical results for contingency tables and robustness, multinomials, flattening constants, and mixed Dirichlet priors, entropy and likelihood, and test as measurement of entropy. The selection is a valuable reference for researchers interested in robust inference and analysis of data sets.
Author: United States
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages :
Get Book
Book Description
Author: George E. P. Box
Publisher:
ISBN: 9780121211608
Category : Mathematical statistics
Languages : en
Pages : 0
Get Book
Book Description
Author: Georgy L. Shevlyakov
Publisher: Walter de Gruyter
ISBN: 3110936003
Category : Mathematics
Languages : en
Pages : 325
Get Book
Book Description
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Author: Robert P. Haining
Publisher: Cambridge University Press
ISBN: 9780521448666
Category : Mathematics
Languages : en
Pages : 436
Get Book
Book Description
Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory analyses. The book also examines spatial referencing, and methods for detecting problems, assessing their seriousness and taking appropriate action are discussed. This is an important text for any discipline requiring a broad overview of current theoretical and applied work for the analysis of spatial data sets. It will be of particular use to research workers and final year undergraduates in the fields of geography, environmental sciences and social sciences.
Author: Rand R. Wilcox
Publisher: Academic Press
ISBN: 0127515429
Category : Mathematics
Languages : en
Pages : 610
Get Book
Book Description
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations. Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables. * Covers latest developments in robust regression * Covers latest improvements in ANOVA * Includes newest rank-based methods * Describes and illustrated easy to use software
Author: D.A. Sprott
Publisher: Springer Science & Business Media
ISBN: 0387227660
Category : Mathematics
Languages : en
Pages : 254
Get Book
Book Description
A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample information into various parts, such that the diverse problems of inference that arise from repeatable experiments may be addressed. A particularly valuable feature is the large number of practical examples, many of which use data taken from experiments published in various scientific journals. This book evolved from the authors own courses on statistical inference, and assumes an introductory course in probability, including the calculation and manipulation of probability functions and density functions, transformation of variables and the use of Jacobians. While this is a suitable text book for advanced undergraduate, Masters, and Ph.D. statistics students, it may also be used as a reference book.
Author: Stephan Morgenthaler
Publisher: Birkhauser
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 304
Get Book
Book Description
The book serves as an insightful and useful companion for students interested in research or scientists who want to learn about modern developments in the field of data analysis.
Author: Andrew Gelman
Publisher: CRC Press
ISBN: 1439898200
Category : Mathematics
Languages : en
Pages : 663
Get Book
Book Description
Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied
Author: Ding-Geng Chen
Publisher: Springer
ISBN: 9811025940
Category : Mathematics
Languages : en
Pages : 222
Get Book
Book Description
This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.
