Häftad, Engelska, 1985
940 kr
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Stephen E. Fienberg – författare
Visar alla böcker från författaren . Handla med fri frakt och snabb leverans.
32 produkter
32 produkter
- E-bokEngelska, 2002
403 kr
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Federal law prohibits housing discrimination on the basis of seven protected classes including race. Despite 30 years of legal prohibition under the Fair Housing Act, however, there is evidence of continuing discrimination in American housing, as documented by several recent reports. In 1998, the Department of Housing and Urban Development (HUD) funded a $7.5 million independently conducted Housing Discrimination Survey (HDS) of racial and ethnic discrimination in housing rental, sales, and lending markets (Public Law 105-276). This survey is the third such effort sponsored by HUD. Its intent is to provide a detailed understanding of the patterns of discrimination in housing nationwide.
In 1999, the Committee on National Statistics (CNSTAT) of the National Research Council (NRC) was asked to review the research design and analysis plan for the 2000 HDS and to offer suggestions about appropriate sampling and analysis procedures. The review took the form of a workshop that addressed HUD''s concerns about the adequacy of the sample design and analysis plan, as well as questions related to the measurement of various aspects of discrimination and issues that might bias the results obtained. The discussion also explored alternative methodologies and research needs. In addition to addressing methodological and substantive issues related specifically to the HDS, the workshop examined broader questions related to the measurement of discrimination.
- E-bokPDF, Engelska, 2002
277 kr
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Federal law prohibits housing discrimination on the basis of seven protected classes including race. Despite 30 years of legal prohibition under the Fair Housing Act, however, there is evidence of continuing discrimination in American housing, as documented by several recent reports. In 1998, the Department of Housing and Urban Development (HUD) funded a $7.5 million independently conducted Housing Discrimination Survey (HDS) of racial and ethnic discrimination in housing rental, sales, and lending markets (Public Law 105-276). This survey is the third such effort sponsored by HUD. Its intent is to provide a detailed understanding of the patterns of discrimination in housing nationwide.
In 1999, the Committee on National Statistics (CNSTAT) of the National Research Council (NRC) was asked to review the research design and analysis plan for the 2000 HDS and to offer suggestions about appropriate sampling and analysis procedures. The review took the form of a workshop that addressed HUD''s concerns about the adequacy of the sample design and analysis plan, as well as questions related to the measurement of various aspects of discrimination and issues that might bias the results obtained. The discussion also explored alternative methodologies and research needs. In addition to addressing methodological and substantive issues related specifically to the HDS, the workshop examined broader questions related to the measurement of discrimination.
- E-bokPDF, Engelska, 1985
839 kr
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- E-bokPDF, Engelska, 1992
522 kr
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Bilingual education has long been the subject of major disagreements in this country. This book provides a detailed critique of the two largest studies of U.S. bilingual education programs. It examines the goals of the studies and what can be learned from them. In addition, using these studies as cases, this book provides guidelines on how to plan large evaluation studies to achieve useful answers to major policy questions about education.
- Häftad, Engelska, 2021
1 128 kr
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In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology.Through examples using real data sets, you’ll discover how to characterize complex multivariate data in:Studies involving genetic databasesPatterns in the progression of diseases and disabilities Combinations of topics covered by text documentsPolitical ideology or electorate voting patternsHeterogeneous relationships in networks, and much moreThe handbook spans more than 20 years of the editors’ and contributors’ statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models. - Inbunden, Engelska, 2006
1 084 kr
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Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume brings together 40 of his most original and influential papers, capturing the variety and depth of his writings. The editors hope to share these with a new generation of researchers, so that they can build upon his insights and efforts.This volume of selected papers is a companion to the earlier volume A Statistical Model: Frederick Mosteller's Contributions to Statistics, Science, and Public Policy, edited by Stephen E. Fienberg, David C. Hoaglin, William H. Kruskal, and Judith M. Tanur (Springer-Verlag, 1990), and to Mosteller's forthcoming autobiography, which will also be published by Springer-Verlag. It includes a biography and a comprehensive bibliography of Mosteller's books, papers, and other writings.Stephen E. Fienberg is Maurice Falk University Professor of Statistics and Social Science, in the Departments of Statistics and Machine Learning at Carnegie Mellon University, Pittsburgh, PA. David C. Hoaglin is Principal Scientist at Abt Associates Inc., Cambridge, MA. - E-bokPDF, Engelska, 2007
1 455 kr
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Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume brings together 40 of his most original and influential papers, capturing the variety and depth of his writings. The editors hope to share these with a new generation of researchers, so that they can build upon his insights and efforts.
This volume of selected papers is a companion to the earlier volume A Statistical Model: Frederick Mosteller''s Contributions to Statistics, Science, and Public Policy, edited by Stephen E. Fienberg, David C. Hoaglin, William H. Kruskal, and Judith M. Tanur (Springer-Verlag, 1990), and to Mosteller''s forthcoming autobiography, which will also be published by Springer-Verlag. It includes a biography and a comprehensive bibliography of Mosteller''s books, papers, and other writings.
Stephen E. Fienberg is Maurice Falk University Professor of Statistics and Social Science, in the Departments of Statistics and Machine Learning at Carnegie Mellon University, Pittsburgh, PA. David C. Hoaglin is Principal Scientist at Abt Associates Inc., Cambridge, MA.
- Häftad, Engelska, 2007
1 407 kr
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6. 2 The Two-Sample Capture-Recapture Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 6. 3 Conditional Maximum Likelihood Estimation of N . . . . . . . . . . . . . . . . . . . . . . 236 6. 4 The Three-Sample Census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 6. 5 The General Multiple Recapture Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 6. 6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 7 MODELS FOR MEASURING CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 7. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 7. 2 First-Order Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 7. 3 Higher-Order Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 7. 4 Markov Models with a Single Sequence of Transitions . . . . . . . . . . . . . . . . . . 270 7. 5 Other Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 8 ANALYSIS OF SQUARE TABLES: SYMMETRY AND MARGINAL HOMOGENEITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 8. 1 Introduction . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 8. 2 Two-Dimensional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 8. 3 Three-Dimensional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 8. 4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 9 MODEL SELECTION AND ASSESSING CLOSENESS OF FIT: PRACTICAL ASPECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 9. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 9. 2 Simplicity in Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 9. 3 Searching for Sampling Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 9. 4 Fitting and Testing Using the Same Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 9. 5 Too Good a Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 9. 6 Large Sample Sizes and Chi Square When the Null Model is False . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 9. 7 Data Anomalies and Suppressing Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 9. 8 Frequency of Frequencies Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 10 OTHER METHODS FOR ESTIMATION AND TESTING IN CROSS-CLASSIFICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 10. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 10. 2 The Information-Theoretic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 10. 3 Minimizing Chi Square, Modi? ed Chi Square, and Logit Chi Square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 10. 4 The Logistic Model and How to Use It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 10. 5 Testing via Partitioning of Chi Square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 10. 6 Exact Theory for Tests Based on Conditional Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 10. 7 Analyses Based on Transformed Proportions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - E-bokPDF, Engelska, 2007
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6. 2 The Two-Sample Capture-Recapture Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 6. 3 Conditional Maximum Likelihood Estimation of N . . . . . . . . . . . . . . . . . . . . . . 236 6. 4 The Three-Sample Census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 6. 5 The General Multiple Recapture Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 6. 6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 7 MODELS FOR MEASURING CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 7. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 7. 2 First-Order Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 7. 3 Higher-Order Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 7. 4 Markov Models with a Single Sequence of Transitions . . . . . . . . . . . . . . . . . . 270 7. 5 Other Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 8 ANALYSIS OF SQUARE TABLES: SYMMETRY AND MARGINAL HOMOGENEITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 8. 1 Introduction . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 8. 2 Two-Dimensional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 8. 3 Three-Dimensional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 8. 4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 9 MODEL SELECTION AND ASSESSING CLOSENESS OF FIT: PRACTICAL ASPECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 9. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 9. 2 Simplicity in Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 9. 3 Searching for Sampling Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 9. 4 Fitting and Testing Using the Same Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 9. 5 Too Good a Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 9. 6 Large Sample Sizes and Chi Square When the Null Model is False . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 9. 7 Data Anomalies and Suppressing Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 9. 8 Frequency of Frequencies Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 10 OTHER METHODS FOR ESTIMATION AND TESTING IN CROSS-CLASSIFICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 10. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 10. 2 The Information-Theoretic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 10. 3 Minimizing Chi Square, Modi? ed Chi Square, and Logit Chi Square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 10. 4 The Logistic Model and How to Use It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 10. 5 Testing via Partitioning of Chi Square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 10. 6 Exact Theory for Tests Based on Conditional Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 10. 7 Analyses Based on Transformed Proportions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - Häftad, Engelska, 2007
546 kr
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A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation. - E-bokPDF, Engelska, 2007
710 kr
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A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation. - Häftad, Engelska, 2010
1 192 kr
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From his unique perspective, renowned statistician and educator Frederick Mosteller describes many of the projects and events in his long career. From humble beginnings in western Pennsylvania to becoming the founding chairman of Harvard University’s Department of Statistics and beyond, he inspired many statisticians, scientists, and students with his unabashed pragmatism, creative thinking, and zest for both learning and teaching. This candid account offers fresh insights into the qualities that made Mosteller a superb teacher, a prolific scholar, a respected leader, and a valued advisor.A special feature of the book is its chapter-length insider accounts of work on the pre-election polls of 1948, statistical aspects of the Kinsey report on sexual behavior in the human male, mathematical learning theory, authorship of the disputed Federalist papers, safety of anesthetics, and a wide-ranging examination of the Coleman report on equality of educational opportunity.This volume is a companion to Selected Papers of Frederick Mosteller (Springer, 2006) and A Statistical Model: Frederick Mosteller’s Contributions to Statistics, Science, and Public Policy (Springer-Verlag, 1990).Frederick Mosteller (1916–2006) was Roger I. Lee Professor of Mathematical Statistics at Harvard University. His manuscript was unfinished at his death and has been updated. - E-bokPDF, Engelska, 2010
1 568 kr
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From his unique perspective, renowned statistician and educator Frederick Mosteller describes many of the projects and events in his long career. From humble beginnings in western Pennsylvania to becoming the founding chairman of Harvard University’s Department of Statistics and beyond, he inspired many statisticians, scientists, and students with his unabashed pragmatism, creative thinking, and zest for both learning and teaching. This candid account offers fresh insights into the qualities that made Mosteller a superb teacher, a prolific scholar, a respected leader, and a valued advisor.
A special feature of the book is its chapter-length insider accounts of work on the pre-election polls of 1948, statistical aspects of the Kinsey report on sexual behavior in the human male, mathematical learning theory, authorship of the disputed Federalist papers, safety of anesthetics, and a wide-ranging examination of the Coleman report on equality of educational opportunity.
This volume is a companion to Selected Papers of Frederick Mosteller (Springer, 2006) and A Statistical Model: Frederick Mosteller’s Contributions to Statistics, Science, and Public Policy (Springer-Verlag, 1990).
Frederick Mosteller (1916–2006) was Roger I. Lee Professor of Mathematical Statistics at Harvard University. His manuscript was unfinished at his death and has been updated.
- Del 1 - Lecture Notes in Statistics
R.A. Fisher: An Appreciation
Häftad, Engelska, 19801 515 kr
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From the reviews: "This collection of essays surveys the most important of Fisher's papers in various areas of statistics. ... ... the monograph will be a useful source of reference to most of Fisher's major papers; it will certainly provide background material for much vigorous discussion." #Australian Journal of Statistics#1 - Häftad, Engelska, 1997
384 kr
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This is author-approved bcc. If it is too long, delete the last sentence in each of the biographies. THE BELL CURVE by Richard Herrnstein and Charles Murray, a best selling book published in 1994, set off a hailstorm of controversy about the relationships among IQ, genetics, and various social outcomes, including welfare dependency, crime, and earnings. Much of the public reaction to the book was polemical and did not focus on the details of the science and in particular on the validity of the statistical arguments that underlie the books's conclusions. A detailed understanding of the arguments in THE BELL CURVE requires knowledge about (i) statistical models for genetic heritability, (ii) factor analysis, especially as it has been applied to the analysis of IQ tests, (iii) logistic regression and multiple regression analyses,and (iv) causal modelling and alternative statistical frameworks for making inference from longitudinal data. In this volume a group of statisticians and social scientists have assembled a scientific response to THE BELL CURVE. The sixteen chapters begin by presenting an overview of the scientific and statistical issues and summarize the material in Herrnstein and Murray's book. Then separate chapters by various experts deal with more focused issues, including reanalyses of data relied upon by the authors of THE BELL CURVE. The final chapters consider some of the implications of the work described in the book for American public policy and scientific research. BERNIE DEVLIN is Program Director of the Computational Genetics Program at the University of Pittsburgh Medical Center. He serves on - Del 53 - Wiley Classics Library
Statistics and the Law
Häftad, Engelska, 19942 057 kr
Skickas inom 5-8 vardagar
Describes a wide variety of applications of statistical concepts in legal settings, as well as cases in which statistical analyses were important elements. Several chapters are devoted to employment discrimination and antitrust violation, two areas of litigation that rely heavily on statistics. Contributors discuss the correct choice and use of statistical techniques in the assessment of damages, the measurement and quantification of skill (in a case deciding whether video poker games require skill), the determination of disputed paternity and elections, and other areas where statistics are used in legal cases. Many of the chapters are written by the statisticians who actually participated in the cases under discussion. - E-bokEngelska, 2014
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Incorporating more than 20 years of the editors'' and contributors'' statistical work in mixed membership modeling, this handbook shows how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, readers will discover how to characterize complex multivariate data in a range of areas. - E-bokPDF, Engelska, 2013
474 kr
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This is author-approved bcc. If it is too long, delete the last sentence in each of the biographies. THE BELL CURVE by Richard Herrnstein and Charles Murray, a best selling book published in 1994, set off a hailstorm of controversy about the relationships among IQ, genetics, and various social outcomes, including welfare dependency, crime, and earnings. Much of the public reaction to the book was polemical and did not focus on the details of the science and in particular on the validity of the statistical arguments that underlie the books''s conclusions. A detailed understanding of the arguments in THE BELL CURVE requires knowledge about (i) statistical models for genetic heritability, (ii) factor analysis, especially as it has been applied to the analysis of IQ tests, (iii) logistic regression and multiple regression analyses,and (iv) causal modelling and alternative statistical frameworks for making inference from longitudinal data. In this volume a group of statisticians and social scientists have assembled a scientific response to THE BELL CURVE. The sixteen chapters begin by presenting an overview of the scientific and statistical issues and summarize the material in Herrnstein and Murray''s book. Then separate chapters by various experts deal with more focused issues, including reanalyses of data relied upon by the authors of THE BELL CURVE. The final chapters consider some of the implications of the work described in the book for American public policy and scientific research. BERNIE DEVLIN is Program Director of the Computational Genetics Program at the University of Pittsburgh Medical Center. He serves on - E-bokPDF, Engelska, 2012
710 kr
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A large number of Mostellar''s friends, colleagues, collaborators, and former students have contributed to the preparation of this volume in honor of his 70th birthday. It provides a critical assessment of Mosteller''s professional and research contributions to the field of statistics and its applications. - E-bokPDF, Engelska, 2012
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With increasing frequency, the proof of facts in legal proceedings en tails the use of quantitative methods. Judges, lawyers, statisticians, social scientists, and many others involved in judicial processes must address is sues such as the evaluation and interpretation of quantitative evidence, the ethical and professional obligations of expert witnesses, and the roles of court-appointed witnesses. The Panel on Statistical Assessments as Evi dence in the Courts was convened to help clarify these issues and provide some guidance in addressing the difficulties encountered in the use of quan titative assessments in legal proceedings. This report is the culmination of more than three years of research and deliberation. In it, we address a variety of issues that arise in federal and state court proceedings when statistical assessments such as quantitative descriptions, causal inferences, and predictions of events based on earlier occurrences are presented as evidence. We appraise the forms in which such assessments are presented, aspects of their admission into evidence, and the response to and evaluation of them by judges and juries. - E-bokPDF, Engelska, 2012
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From the reviews: "This collection of essays surveys the most important of Fisher''s papers in various areas of statistics. ... ... the monograph will be a useful source of reference to most of Fisher''s major papers; it will certainly provide background material for much vigorous discussion." #Australian Journal of Statistics#1 - Häftad, Engelska, 2011
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A large number of Mostellar's friends, colleagues, collaborators, and former students have contributed to the preparation of this volume in honor of his 70th birthday. It provides a critical assessment of Mosteller's professional and research contributions to the field of statistics and its applications. - Häftad, Engelska, 2011
503 kr
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With increasing frequency, the proof of facts in legal proceedings en tails the use of quantitative methods. Judges, lawyers, statisticians, social scientists, and many others involved in judicial processes must address is sues such as the evaluation and interpretation of quantitative evidence, the ethical and professional obligations of expert witnesses, and the roles of court-appointed witnesses. The Panel on Statistical Assessments as Evi dence in the Courts was convened to help clarify these issues and provide some guidance in addressing the difficulties encountered in the use of quan titative assessments in legal proceedings. This report is the culmination of more than three years of research and deliberation. In it, we address a variety of issues that arise in federal and state court proceedings when statistical assessments such as quantitative descriptions, causal inferences, and predictions of events based on earlier occurrences are presented as evidence. We appraise the forms in which such assessments are presented, aspects of their admission into evidence, and the response to and evaluation of them by judges and juries. - E-bokPDF, Engelska, 2012
710 kr
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The International Statistical Institute was founded in 1885 and is therefore one of the world''s oldest international scientific societies. The field of statistics is still expanding rapidly and possesses a rich variety of applications in many areas of human activity such as science, government, business, industry, and everyday affairs. In consequence, the celebration of the Institute''s centenary in 1985 is of considerable interest not only to statisticians but also more widely to the international scientific community. As part of its centennial celebration planning the Institute decided to publish a volume of papers representing the immensely wide range of interests encompassed by statistics in its international context, viewed both from a historical and from a contemporary standpoint. We were fortunate in securing the services of Anthony Atkinson and Stephen Fienberg as Editors of this volume: they have worked hard over a period of several years to put together a most fascinating collection of papers. On behalf of the Institute it is my pleasant duty to thank them and the authors for their contributions. J. DURBIN, President International Statistical Institute Preface The papers in this volume were prepared to help celebrate the centenary of the International Statistical Institute. During the lSI''s first 100 years statistics has matured, both as a scientific discipline and as a profession, in ways that the lSI''s founders could not possibly have imagined. - Häftad, Engelska, 2011
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The International Statistical Institute was founded in 1885 and is therefore one of the world's oldest international scientific societies. The field of statistics is still expanding rapidly and possesses a rich variety of applications in many areas of human activity such as science, government, business, industry, and everyday affairs. In consequence, the celebration of the Institute's centenary in 1985 is of considerable interest not only to statisticians but also more widely to the international scientific community. As part of its centennial celebration planning the Institute decided to publish a volume of papers representing the immensely wide range of interests encompassed by statistics in its international context, viewed both from a historical and from a contemporary standpoint. We were fortunate in securing the services of Anthony Atkinson and Stephen Fienberg as Editors of this volume: they have worked hard over a period of several years to put together a most fascinating collection of papers. On behalf of the Institute it is my pleasant duty to thank them and the authors for their contributions. J. DURBIN, President International Statistical Institute Preface The papers in this volume were prepared to help celebrate the centenary of the International Statistical Institute. During the lSI's first 100 years statistics has matured, both as a scientific discipline and as a profession, in ways that the lSI's founders could not possibly have imagined. - Häftad, Engelska, 2014
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Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume brings together 40 of his most original and influential papers, capturing the variety and depth of his writings. The editors hope to share these with a new generation of researchers, so that they can build upon his insights and efforts.This volume of selected papers is a companion to the earlier volume A Statistical Model: Frederick Mosteller's Contributions to Statistics, Science, and Public Policy, edited by Stephen E. Fienberg, David C. Hoaglin, William H. Kruskal, and Judith M. Tanur (Springer-Verlag, 1990), and to Mosteller's forthcoming autobiography, which will also be published by Springer-Verlag. It includes a biography and a comprehensive bibliography of Mosteller's books, papers, and other writings.Stephen E. Fienberg is Maurice Falk University Professor of Statistics and Social Science, in the Departments of Statistics and Machine Learning at Carnegie Mellon University, Pittsburgh, PA. David C. Hoaglin is Principal Scientist at Abt Associates Inc., Cambridge, MA. - Inbunden, Engelska, 2014
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In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology.Through examples using real data sets, you’ll discover how to characterize complex multivariate data in:Studies involving genetic databasesPatterns in the progression of diseases and disabilities Combinations of topics covered by text documentsPolitical ideology or electorate voting patternsHeterogeneous relationships in networks, and much moreThe handbook spans more than 20 years of the editors’ and contributors’ statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models. - E-bokPDF, Engelska, 2014
1 345 kr
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Incorporating more than 20 years of the editors'' and contributors'' statistical work in mixed membership modeling, this handbook shows how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, readers will discover how to characterize complex multivariate data in a range of areas. - Häftad, Engelska, 2007
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This volume was prepared to share with a larger audience the exciting ideas and work presented at an ICML 2006 workshop of the same title. Network models have a long history. Sociologists and statisticians made major advances in the 1970s and 1980s, culminating in part with a number of substantial databases and the class of exponential random graph models and related methods in the early 1990s. Physicists and computer scientists came to this domain cons- erably later, but they enriched the array of models and approaches and began to tackle much larger networks and more complex forms of data. Our goal in organ- ing the workshop was to encourage a dialog among people coming from di?erent disciplinary perspectives and with di?erent methods, models, and tools. Both the workshop and the editing of the proceedings was a truly colla- rative e?ort on behalf of all six editors, but three in particular deserve special recognition. Anna Goldenberg and Alice Zheng were the driving force behind the entire enterprise and Edo Airoldi assisted on a number of the more important arrangements.
