Models for Intensive Longitudinal Data
Theodore A. Walls, Joseph L. Schafer
Oxford University Press, Jan 19, 2006 - Mathematics - 320 pages
Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD). Behavioral scientific studies now frequently utilize handheld computers, beepers, web interfaces, and other technological tools for collecting many more data points over time than previously possible. Other protocols, such as those used in fMRI and monitoring of public safety, also produce ILD, hence the statistical models in this volume are applicable to a range of data. The volume features state-of-the-art statistical modeling strategies developed by leading statisticians and methodologists working on ILD in conjunction with behavioral scientists. Chapters present applications from across the behavioral and health sciences, including coverage of substantive topics such as stress, smoking cessation, alcohol use, traffic patterns, educational performance and intimacy. Models for Intensive Longitudinal Data (MILD) is designed for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters highlight issues of general concern in modeling these kinds of data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. The extraordinary breadth of coverage makes this an indispensable reference for principal investigators designing new studies that will introduce ILD, applied statisticians working on related models, and methodologists, graduate students, and applied analysts working in a range of fields. A companion Web site at www.oup.com/us/MILD contains program examples and documentation.
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1 Multilevel Models for Intensive Longitudinal Data
2 Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations
3 A Local Linear Estimation Procedure for Functional Multilevel Modeling
4 Application of Item Response Theory Models for Intensive Longitudinal Data
5 Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data
6 Multilevel Autoregressive Modeling of Interindividual Differences in the Stability of a Process
7 The StateSpace Approach to Modeling Dynamic Processes
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ˆβ accelerometer algorithm application assumed assumptions autoregressive average B-spline chapter computing correlation covariance matrix covariance structure curve Day of week described differential equation EM algorithm equilibrium example figure fixed effects fMRI functional data analysis functional multilevel model Hierarchical Linear Models individual intensity function intensive longitudinal data intercept intervals intimacy IRT models Item Response Theory Journal Kalman filter linear mixed model linear models linear regression maximum likelihood estimator mean methods multilevel model multivariate negative affect nicotine observations occasions parameters pattern period point process point process models Poisson process Psychology random effects Rasch model regression coefficients regression model residuals response sample score self-regulation semivariogram sensor Shiffman slope smoking rate smoothing spline standard errors state-space models Statistical supervised learning three-level model time-varying covariates trajectory trend urge to smoke values variable variance vector zero