Multiple and Generalized Nonparametric Regression

Author: John Fox

Publisher: SAGE

ISBN: 9780761921899

Category: Social Science

Page: 83

View: 643

This book builds on John Fox's previous volume in the QASS Series, Non Parametric Simple Regression. In this book, the reader learns how to estimate and plot smooth functions when there are multiple independent variables.
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Nonparametric Simple Regression

Smoothing Scatterplots

Author: John Fox

Publisher: SAGE

ISBN: 9780761915850

Category: Mathematics

Page: 83

View: 5799

John Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit.
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Applied Regression Analysis and Generalized Linear Models

Author: John Fox

Publisher: SAGE Publications

ISBN: 1483321312

Category: Social Science

Page: 816

View: 9083

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
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Multiple Time Series Models

Author: Patrick T. Brandt,John T. Williams

Publisher: SAGE

ISBN: 1412906563

Category: Mathematics

Page: 99

View: 9942

Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models.
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The Association Graph and the Multigraph for Loglinear Models

Author: Harry J. Khamis

Publisher: SAGE

ISBN: 1452238952

Category: Mathematics

Page: 136

View: 7909

The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.
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Regression

Modelle, Methoden und Anwendungen

Author: Ludwig Fahrmeir,Thomas Kneib,Stefan Lang

Publisher: Springer-Verlag

ISBN: 3642018378

Category: Business & Economics

Page: 502

View: 8625

In dem Band beschreiben die Autoren erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten und anwendungsorientierten Form. Um Lesern die Analyse eigener Fragestellungen zu ermöglichen, demonstrieren sie die praktische Anwendung der Konzepte und Methoden anhand ausführlicher Fallstudien. Geeignet für Studierende der Statistik sowie für Wissenschaftler und Praktiker, zum Beispiel in den Wirtschafts- und Sozialwissenschaften, der Bioinformatik und -statistik, Ökonometrie und Epidemiologie.
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Multivariate General Linear Models

Author: Richard F. Haase

Publisher: SAGE

ISBN: 1412972493

Category: Mathematics

Page: 216

View: 7717

This book provides a graduate level introduction to multivariate multiple regression analysis. The book can be used as a sole text for that topic, or as a supplemental text in a course that addresses a larger number of multivariate topics. The text is divided into seven short chapters. Apart from the introductory chapter giving an overview of multivariate multiple regression models, the content outline follows the classic steps required to solve multivariate general linear model problems: (a) specifying the model (b)estimating the parameters of the model (c) establishing measures of goodness of fit of the model (d) establishing test statistics and testing hypotheses about the model (e) diagnosing the adequacy of the model.
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Quantile Regression

Author: Lingxin Hao,Daniel Q. Naiman

Publisher: SAGE Publications

ISBN: 1483316904

Category: Social Science

Page: 136

View: 8920

Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research
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Applied Statistics: From Bivariate Through Multivariate Techniques

From Bivariate Through Multivariate Techniques

Author: Rebecca M. Warner

Publisher: SAGE

ISBN: 141299134X

Category: Mathematics

Page: 1172

View: 8069

Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.
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A Mathematical Primer for Social Statistics

Author: John Fox

Publisher: SAGE

ISBN: 1412960800

Category: Mathematics

Page: 170

View: 5787

Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.
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Internet Data Collection

Author: Samuel J. Best,Brian S. Krueger

Publisher: SAGE

ISBN: 9780761927105

Category: Computers

Page: 91

View: 3372

Designed for researchers and students alike, the volume describes how to perform each stage of the data collection process on the Internet, including sampling, instrument design, and administration. Through the use of non-technical prose and illustrations, it details the options available, describes potential dangers in choosing them, and provides guidelines for sidestepping them. In doing so, though, it does not simply reiterate the practices of traditional communication modes, but approaches the Internet as a unique medium that necessitates its own conventions.
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Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

Author: Julian J. Faraway

Publisher: CRC Press

ISBN: 9780203492284

Category: Mathematics

Page: 312

View: 4004

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/ Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.
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Methoden der Politikwissenschaft

Neuere qualitative und quantitative Analyseverfahren

Author: Joachim Behnke,Thomas Gschwend,Delia Schindler,Kai-Uwe Schnapp

Publisher: N.A

ISBN: N.A

Category: Political science

Page: 364

View: 3206

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Observed Confidence Levels

Theory and Application

Author: Alan M. Polansky

Publisher: CRC Press

ISBN: 9781584888031

Category: Mathematics

Page: 288

View: 9512

Illustrating a simple, novel method for solving an array of statistical problems, Observed Confidence Levels: Theory and Application describes the basic development of observed confidence levels, a methodology that can be applied to a variety of common multiple testing problems in statistical inference. It focuses on the modern nonparametric framework of bootstrap-based estimates, allowing for substantial theoretical development and for relatively simple solutions to numerous interesting problems. After an introduction, the book develops the theory and application of observed confidence levels for general scalar parameters, vector parameters, and linear models. It then examines nonparametric problems often associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized linear models, classical nonparametric statistics, multivariate analysis, and survival analysis as well as compares the method of observed confidence levels to hypothesis testing, multiple comparisons, and Bayesian posterior probabilities. In addition, the appendix presents some background material on the asymptotic expansion theory used in the book. Helping you choose the most reliable method for a variety of problems, this book shows how observed confidence levels provide useful information on the relative truth of hypotheses in multiple testing problems.
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Modern methods of data analysis

Author: John Fox,J. Scott Long

Publisher: Sage Publications, Inc

ISBN: N.A

Category: Mathematics

Page: 446

View: 3539

This volume seeks to move the standard of statistical analysis and presentation in the social sciences towards an accurate and sensitive representation of data. This is accomplished by focusing on four themes: an emphasis on graphical data analysis; the use of computers for doing intensive computations; regression analysis; and sampling characteristics of data.
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Statistical computing environments for social research

Author: Robert A. Stine,John Fox

Publisher: Sage Publications, Inc

ISBN: 9780761902690

Category: Social Science

Page: 250

View: 3553

The nature of statistics has changed from classical notions of hypothesis testing, towards graphical and exploratory data analysis which exploits the flexibility of interactive computing and graphical displays. This book describes seven statistical computing environments - APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS//IML, and Stata - which can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to standard statistical packages, provide programming tools for building other statistical applications. Programmability, flexible data structures, and - in the case of some of the computing environments - graphical interfaces and object-oriented programming, permit res
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Statistical Regression and Classification

From Linear Models to Machine Learning

Author: Norman Matloff

Publisher: CRC Press

ISBN: 1351645897

Category: Business & Economics

Page: 490

View: 4939

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
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Knowledge based systems in medicine

methods, applications, and evaluation : proceedings of the Workshop "System Engineering in Medicine," Maastricht March 16-18, 1989

Author: Jan L. Talmon,Rijksuniversiteit Limburg. Dept. of Medical Informatics,Commission of the European Communities,Commission of the European Communities. Medical and Health Research Programme

Publisher: Springer

ISBN: 9783540550112

Category: Computers

Page: 330

View: 7853

This book contains 25 papers describing methods, applications and evaluationmethodologies in the domain of artificial intelligence in medicine. The section on methods covers knowledge acquisition methods and various modelling techniques relevant for building medical decision aids. The application section describes several systems for the management of the chronically and the critically ill. Several aspects of the evaluation ofmedical decision aids are discussed in 5 papers that comprise the final section of the book. The book contains relevant information for system developers as well as potential users of medical decision aids.
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Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models

Author: Myoung-jae Lee

Publisher: Springer Science & Business Media

ISBN: 1475725507

Category: Business & Economics

Page: 279

View: 668

In this book the author surveys new techniques in econometrics which may be used to analyse semiparametric models. As well as covering topics such as instrumental variable estimation, nonparametric density and regression function estimation and semiparametric limited dependent variable models, the book provides details of how these methods may be implemented using software.
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