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Latent Class And Latent Transition Analysis

Author: Linda M. Collins
Publisher: John Wiley & Sons
ISBN: 111821076X
Size: 43.19 MB
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A modern, comprehensive treatment of latent class and latenttransition analysis for categorical data On a daily basis, researchers in the social, behavioral, andhealth sciences collect information and fit statistical models tothe gathered empirical data with the goal of making significantadvances in these fields. In many cases, it can be useful toidentify latent, or unobserved, subgroups in a population, whereindividuals' subgroup membership is inferred from their responseson a set of observed variables. Latent Class and LatentTransition Analysis provides a comprehensive and unifiedintroduction to this topic through one-of-a-kind, step-by-steppresentations and coverage of theoretical, technical, and practicalissues in categorical latent variable modeling for bothcross-sectional and longitudinal data. The book begins with an introduction to latent class and latenttransition analysis for categorical data. Subsequent chapters delveinto more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings ofinterpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identificationproblems Advanced topics such as multi-group analysis and the modelingand interpretation of interactions between covariates The authors present the topic in a style that is accessible yetrigorous. Each method is presented with both a theoreticalbackground and the practical information that is useful for anydata analyst. Empirical examples showcase the real-worldapplications of the discussed concepts and models, and each chapterconcludes with a "Points to Remember" section that contains a briefsummary of key ideas. All of the analyses in the book are performedusing Proc LCA and Proc LTA, the authors' own software packagesthat can be run within the SAS® environment. A related Website houses information on these freely available programs and thebook's data sets, encouraging readers to reproduce the analyses andalso try their own variations. Latent Class and Latent Transition Analysis is anexcellent book for courses on categorical data analysis and latentvariable models at the upper-undergraduate and graduate levels. Itis also a valuable resource for researchers and practitioners inthe social, behavioral, and health sciences who conduct latentclass and latent transition analysis in their everyday work.

Analysis Of Financial Time Series

Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 9781118017098
Size: 62.56 MB
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This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Statistical Methods For Survival Data Analysis

Author: Elisa T. Lee
Publisher: John Wiley & Sons
ISBN: 1118593057
Size: 15.22 MB
Format: PDF, ePub
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Praise for the Third Edition “. . . an easy-to read introduction to survival analysiswhich covers the major concepts and techniques of thesubject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments,Statistical Methods for Survival Data Analysis, FourthEdition continues to deliver a comprehensive introduction tothe most commonly-used methods for analyzing survival data.Authored by a uniquely well-qualified author team, the FourthEdition is a critically acclaimed guide to statistical methods withapplications in clinical trials, epidemiology, areas of business,and the social sciences. The book features many real-world examplesto illustrate applications within these various fields, althoughspecial consideration is given to the study of survival data inbiomedical sciences. Emphasizing the latest research and providing the mostup-to-date information regarding software applications in thefield, Statistical Methods for Survival Data Analysis, FourthEdition also includes: Marginal and random effect models for analyzing correlatedcensored or uncensored data Multiple types of two-sample and K-sample comparisonanalysis Updated treatment of parametric methods for regression modelfitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of thepresented material Statistical Methods for Survival Data Analysis is anideal text for upper-undergraduate and graduate-level courses onsurvival data analysis. The book is also an excellent resource forbiomedical investigators, statisticians, and epidemiologists, aswell as researchers in every field in which the analysis ofsurvival data plays a role.

Time Series Analysis And Forecasting By Example

Author: Søren Bisgaard
Publisher: John Wiley & Sons
ISBN: 9781118056950
Size: 73.98 MB
Format: PDF, Docs
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An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.

Loss Models

Author: Stuart A. Klugman
Publisher: John Wiley & Sons
ISBN: 1118315324
Size: 36.76 MB
Format: PDF, ePub, Mobi
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"Newly organized to focus exclusively on material tested in the Society of Actuaries' Exam C and the Casualty Actuarial Society's Exam 4, 'Loss models : from data to decisions', fourth edition, continues to supply actuaries with a practical approach to the key concepts and techniques needed on the job."--Back cover.

Applied Linear Regression

Author: Sanford Weisberg
Publisher: John Wiley & Sons
ISBN: 1118594851
Size: 70.45 MB
Format: PDF, Kindle
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Praise for the Third Edition "...this is an excellent book which could easily be used as acourse text..." —International Statistical Institute The Fourth Edition of Applied LinearRegression provides a thorough update of the basic theoryand methodology of linear regression modeling. Demonstrating thepractical applications of linear regression analysis techniques,the Fourth Edition uses interesting, real-worldexercises and examples. Stressing central concepts such as model building, understandingparameters, assessing fit and reliability, and drawing conclusions,the new edition illustrates how to develop estimation, confidence,and testing procedures primarily through the use of least squaresregression. While maintaining the accessible appeal of eachprevious edition,Applied Linear Regression, FourthEdition features: Graphical methods stressed in the initial exploratory phase,analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple andcomplex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, andvariance assumptions Updated methodology, such as bootstrapping, cross-validationbinomial and Poisson regression, and modern model selectionmethods Applied Linear Regression, Fourth Edition is anexcellent textbook for upper-undergraduate and graduate-levelstudents, as well as an appropriate reference guide forpractitioners and applied statisticians in engineering, businessadministration, economics, and the social sciences.

The Analysis Of Covariance And Alternatives

Author: Bradley Huitema
Publisher: John Wiley & Sons
ISBN: 9781118067468
Size: 75.33 MB
Format: PDF, ePub
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A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.