Continuous time modeling in the behavioral and related sciences /
نام عام مواد
[Book]
نام نخستين پديدآور
Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle, editors.
وضعیت نشر و پخش و غیره
محل نشرو پخش و غیره
Cham, Switzerland :
نام ناشر، پخش کننده و غيره
Springer,
تاریخ نشرو بخش و غیره
2018.
مشخصات ظاهری
نام خاص و کميت اثر
1 online resource (xi, 442 pages) :
ساير جزييات
illustrations (some color)
يادداشت کلی
متن يادداشت
Includes index.
یادداشتهای مربوط به مندرجات
متن يادداشت
Intro; Preface; Contents; Contributors; 1 First- and Higher-Order Continuous Time Models for Arbitrary N Using SEM; 1.1 Introduction; 1.2 Continuous Time Model; 1.2.1 Basic Model; 1.2.2 Connecting Discrete and Continuous Time Model in the EDM; 1.2.3 Extended Continuous Time Model; 1.2.4 Exogenous Variables; 1.2.5 Traits; 1.3 Model Estimation by SEM; 1.4 Analysis of Sunspot Data: CARMA(2,1) on N = 1, T = 167; 1.5 Conclusion; References; 2 A Continuous-Time Approach to Intensive Longitudinal Data: What, Why, and How?; 2.1 Introduction; 2.2 Two Frameworks; 2.2.1 The Discrete-Time Framework
متن يادداشت
2.2.2 The Continuous-Time Framework2.2.3 Relating DT and CT Models; 2.2.4 Types of Dynamics: Eigenvalues, Stability, and Equilibrium; 2.3 Why Researchers Should Adopt a CT Process Perspective; 2.4 Making Sense of CT Models; 2.4.1 Substantive Example from Empirical Data; 2.4.2 Interpreting the Drift Parameters; 2.4.3 Visualizing Trajectories; 2.4.3.1 Impulse Response Functions; 2.4.3.2 Vector Fields; 2.4.4 Inspecting the Lagged Parameters; 2.4.5 Caution with Interpreting Estimated Parameters; 2.5 Discussion; 2.5.1 Beyond Two-Dimensional Systems; 2.5.2 Complex and Positive Eigenvalues
متن يادداشت
2.5.3 Multilevel Extensions2.5.4 Conclusion; Appendix: Matrix Exponential; References; 3 On Fitting a Continuous-Time Stochastic Process Model in the Bayesian Framework; 3.1 Introduction; 3.1.1 The Need for Continuous-Time Process Models to Analyze Intensive Longitudinal Data; 3.1.2 The Need for Continuous-Time Process Models to Capture Temporal Changes in Core Affective States; 3.2 The Ornstein-Uhlenbeck Process to Describe Within-Person Latent Temporal Dynamics; 3.2.1 The Stochastic Differential Equation Definition of the Ornstein-Uhlenbeck Process
متن يادداشت
3.2.2 The Position Equation of the Ornstein-Uhlenbeck Process3.2.3 Extending the Ornstein-Uhlenbeck Process to Two Dimensions; 3.2.4 Accounting for Measurement Error; 3.3 A Multilevel/Hierarchical Extension to the Ornstein-Uhlenbeck Process; 3.3.1 Specifying the Population Distribution for the Baseline; 3.3.2 Specifying the Population Distribution for the Regulatory Force; 3.3.3 Specifying the Population Distribution for the BPS Input; 3.4 Casting the Multilevel OU Process Model in the Bayesian Framework
متن يادداشت
3.5 Investigating Core Affect Dynamics with the Bayesian Multilevel Ornstein-Uhlenbeck Process Model3.5.1 A Process Model of Core Affect Dynamics Measured in an Ecological Momentary Assessment Study; 3.5.2 Population-Level Summaries and Individual Differences of Core Affect Dynamics; 3.5.3 Results on the Time-Invariant Covariates; 3.6 Discussion; References; 4 Understanding the Time Course of Interventions with Continuous Time Dynamic Models; 4.1 Introduction; 4.2 The Model; 4.2.1 Latent Dynamic Model; 4.2.2 Discrete Time Solution of Latent Dynamic Model; 4.2.3 Measurement Model
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This unique book provides an overview of continuous time modeling in the behavioral and related sciences. It argues that the use of discrete time models for processes that are in fact evolving in continuous time produces problems that make their application in practice highly questionable. One main issue is the dependence of discrete time parameter estimates on the chosen time interval, which leads to incomparability of results across different observation intervals. Continuous time modeling by means of differential equations offers a powerful approach for studying dynamic phenomena, yet the use of this approach in the behavioral and related sciences such as psychology, sociology, economics and medicine, is still rare. This is unfortunate, because in these fields often only a few discrete time (sampled) observations are available for analysis (e.g., daily, weekly, yearly, etc.). However, as emphasized by Rex Bergstrom, the pioneer of continuous-time modeling in econometrics, neither human beings nor the economy cease to exist in between observations. In 16 chapters, the book addresses a vast range of topics in continuous time modeling, from approaches that closely mimic traditional linear discrete time models to highly nonlinear state space modeling techniques. Each chapter describes the type of research questions and data that the approach is most suitable for, provides detailed statistical explanations of the models, and includes one or more applied examples. To allow readers to implement the various techniques directly, accompanying computer code is made available online. The book is intended as a reference work for students and scientists working with longitudinal data who have a Master's- or early PhD-level knowledge of statistics.--