Time-varying Coefficient Models with ARMA-GARCH Structures for Longitudinal Data Analysis PDF Download
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Author: Haiyan Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Book Description
ABSTRACT: The motivation of my research comes from the analysis of the Framingham Heart Study (FHS) data. The FHS is a long term prospective study of cardiovascular disease in the community of Framingham, Massachusetts. The study began in 1948 and 5,209 subjects were initially enrolled. Examinations were given biennially to the study participants and their status associated with the occurrence of disease was recorded. In this dissertation, the event we are interested in is the incidence of the coronary heart disease (CHD). Covariates considered include sex, age, cigarettes per day (CSM), serum cholesterol (SCL), systolic blood pressure (SBP) and body mass index (BMI, weight in kilograms/height in meters squared).
Author: Haiyan Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Book Description
ABSTRACT: The motivation of my research comes from the analysis of the Framingham Heart Study (FHS) data. The FHS is a long term prospective study of cardiovascular disease in the community of Framingham, Massachusetts. The study began in 1948 and 5,209 subjects were initially enrolled. Examinations were given biennially to the study participants and their status associated with the occurrence of disease was recorded. In this dissertation, the event we are interested in is the incidence of the coronary heart disease (CHD). Covariates considered include sex, age, cigarettes per day (CSM), serum cholesterol (SCL), systolic blood pressure (SBP) and body mass index (BMI, weight in kilograms/height in meters squared).
Author: Peter Diggle
Publisher: Oxford University Press, USA
ISBN: 0199676755
Category : Language Arts & Disciplines
Languages : en
Pages : 397
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Book Description
This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
Author: Jason Newsom
Publisher: Routledge
ISBN: 1136705473
Category : Psychology
Languages : en
Pages : 407
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Book Description
This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.
Author: Jinglan Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
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Book Description
Consider a varying coefficient model (Hastie and Tibshirani, 1993), where the coefficient is unknown but is dynamic in the sense that it is a function of a certain covariate. In some cases, the covariate is a special variable 'time'. Motivated by the need for varying-coefficient vector time series models (Jiang, 1999) and varying-coefficient partially linear models (Fan, Huang, and Li, 2007), we are primarily interested in time-varying coefficient models for continuous multivariate time series data and continuous longitudinal data. The challenge is how to simultaneously display serial, clustering, and multivariate attributes of the data set, to which the routinely assumed two-level and univariate response models are not able to apply. We approach this problem by a flexible new model called multiple response hierarchical time-varying mixed-effects model. So far, the thesis has focused on two responses. Extension to >2 responses involves no fundamentally new ideas. The model first uses varying-coefficient parameters for accurately describing the dynamic of the series. The new covariance matrix is decomposed into between-response correlation structure of random cluster effect and correlation structure between measurement errors. By allowing shared cluster effects the model allows for characterizing homogeneity in repeated measurements in the same cluster. By allowing for time dependent error terms, it is possible to model the correlation induced by within-subject variation. We adopt a similar approach of Fan and Gijbels (1996), where we first propose local linear regression estimators for the varying coefficients, and then obtain random effect prediction by maximizing the profile likelihood with a closed-form solution. Asymptotic results give good insight into the properties of estimators. It is shown that estimates are consistent. We also conduct the model comparison, it turns out that the proposed methods outperform the traditional univariate response models, nonparametric models, and linear mixed effects models in both predicting the response and estimating the coefficient surface based on simulation studies. Finally, we have applied this model to a real-world study on the price-volume relation of NASDAQ stock market data.
Author: Theodore A. Walls
Publisher: Oxford University Press
ISBN: 0195173449
Category : Mathematics
Languages : en
Pages : 311
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Book Description
A new class of longitudinal data has emerged with the use of technological devices for scientific data collection called Intensive Longitudinal Data. This volume features state-of-the-art applied statistical modelling strategies developed by leading statisticians and methodologists.
Author: Professor Catrien C J H C J H Bijleveld
Publisher: SAGE
ISBN: 9781446231586
Category : Social Science
Languages : en
Pages : 462
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Book Description
By looking at the processes of change over time - by carrying out longitudinal studies - researchers answer questions about learning, development, educational growth, social change and medical outcomes. However, longitudinal research has many faces. This book examines all the main approaches as well as newer developments (such as structural equation modelling, multilevel modelling and optimal scaling) to enable the reader to gain a thorough understanding of the approach and make appropriate decisions about which technique can be applied to the research problem. Conceptual explanations are used to keep technical terms to a minimum; examples are provided for each approach; issues of design, measurement and significance are considered; and a standard notation is used throughout.
Author: Garrett Fitzmaurice
Publisher: CRC Press
ISBN: 142001157X
Category : Mathematics
Languages : en
Pages : 633
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Book Description
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory
Author: Robert E. Weiss
Publisher: Springer Science & Business Media
ISBN: 0387283145
Category : Medical
Languages : en
Pages : 445
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Book Description
The book features many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Weiss emphasizes continuous data rather than discrete data, graphical and covariance methods, and generalizations of regression rather than generalizations of analysis of variance.
Author: David J. Hand
Publisher: Routledge
ISBN: 1351422650
Category : Mathematics
Languages : en
Pages : 248
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Book Description
This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.
Author: Lesa Hoffman
Publisher: Routledge
ISBN: 1317591089
Category : Psychology
Languages : en
Pages : 867
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Book Description
Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers’ understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.
