Numerical Issues In Statistical Computing For The Social Scientist

At last—a social scientist's guide through the pitfalls ofmodern statistical computing Addressing the current deficiency in the literature onstatistical methods as they apply to the social and behavioralsciences, Numerical Issues in Statistical Computing for the SocialScientist seeks to provide readers with a unique practicalguidebook to the numerical methods underlying computerizedstatistical calculations specific to these fields. The authorsdemonstrate that knowledge of these numerical methods and how theyare used in statistical packages is essential for making accurateinferences. With the aid of key contributors from both the socialand behavioral sciences, the authors have assembled a rich set ofinterrelated chapters designed to guide empirical social scientiststhrough the potential minefield of modern statisticalcomputing. Uniquely accessible and abounding in modern-day tools, tricks,and advice, the text successfully bridges the gap between thecurrent level of social science methodology and the moresophisticated technical coverage usually associated with thestatistical field. Highlights include: A focus on problems occurring in maximum likelihoodestimation Integrated examples of statistical computing (using softwarepackages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS,WinBUGS, and MATLAB®) A guide to choosing accurate statistical packages Discussions of a multitude of computationally intensivestatistical approaches such as ecological inference, Markov chainMonte Carlo, and spatial regression analysis Emphasis on specific numerical problems, statisticalprocedures, and their applications in the field Replications and re-analysis of published social scienceresearch, using innovative numerical methods Key numerical estimation issues along with the means ofavoiding common pitfalls A related Web site includes test data for use in demonstratingnumerical problems, code for applying the original methodsdescribed in the book, and an online bibliography of Web resourcesfor the statistical computation Designed as an independent research tool, a professionalreference, or a classroom supplement, the book presents awell-thought-out treatment of a complex and multifaceted field.

Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.

Advances in Computer and Information Sciences and Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Advances in Computer and Information Sciences and Engineering includes selected papers from the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2007) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2007).

Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.

The growth of electronic publishing of literature has created new challenges, such as the need for mechanisms for citing online references in ways that can assure discoverability and retrieval for many years into the future. The growth in online datasets presents related, yet more complex challenges. It depends upon the ability to reliably identify, locate, access, interpret, and verify the version, integrity, and provenance of digital datasets. Data citation standards and good practices can form the basis for increased incentives, recognition, and rewards for scientific data activities that in many cases are currently lacking in many fields of research. The rapidly-expanding universe of online digital data holds the promise of allowing peer-examination and review of conclusions or analysis based on experimental or observational data, the integration of data into new forms of scholarly publishing, and the ability for subsequent users to make new and unforeseen uses and analyses of the same data-either in isolation, or in combination with, other datasets. The problem of citing online data is complicated by the lack of established practices for referring to portions or subsets of data. There are a number of initiatives in different organizations, countries, and disciplines already underway. An important set of technical and policy approaches have already been launched by the U.S. National Information Standards Organization (NISO) and other standards bodies regarding persistent identifiers and online linking. The workshop summarized in For Attribution-Developing Data Attribution and Citation Practices and Standards: Summary of an International Workshop was organized by a steering committee under the National Research Council's (NRC's) Board on Research Data and Information, in collaboration with an international CODATA-ICSTI Task Group on Data Citation Standards and Practices. The purpose of the symposium was to examine a number of key issues related to data identification, attribution, citation, and linking to help coordinate activities in this area internationally, and to promote common practices and standards in the scientific community.

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

"This book compiles estimable research on the global trend toward the rapidly increasing use of information technology in the public sector, discussing such issues as e-government and e-commerce; project management and information technology evaluation; system design and data processing; security and protection; and privacy, access, and ethics of public information technology"--Provided by publisher.