Bayesian Data Analysis with the Bivariate Hierarchical Ornstein-Uhlenbeck Process Model
Bibliographic Data
| ID | 19290706 |
|---|---|
| Authors | Zita Oravecz (0000-0002-9070-3329, Pennsylvania State University, corresponding author), Francis Tuerlinckx (0000-0002-1775-7654, KU Leuven), Joachim Vandekerckhove (0000-0003-2600-5937, University of California, Irvine) |
| Year | 2016 |
| Volume | 51 |
| Issue | 1 |
| Pages | 106-119 |
| Publication date | 2016-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2015.1110512 |
| PMID | 26881960 |
| OpenAlex | W2286235005 |
| Language | EN |
| Citations received | 15 |
| References cited | 43 |
In this paper, we propose a multilevel process modeling approach to describing individual differences in within-person changes over time. To characterize changes within an individual, repeated measures over time are modeled in terms of three person-specific parameters: a baseline level, intraindividual variation around the baseline, and regulatory mechanisms adjusting toward baseline. Variation due to measurement error is separated from meaningful intraindividual variation. The proposed model allows for the simultaneous analysis of longitudinal measurements of two linked variables (bivariate longitudinal modeling) and captures their relationship via two person-specific parameters. Relationships between explanatory variables and model parameters can be studied in a one-stage analysis, meaning that model parameters and regression coefficients are estimated simultaneously. Mathematical details of the approach, including a description of the core process model-the Ornstein-Uhlenbeck model-are provided. We also describe a user friendly, freely accessible software program that provides a straightforward graphical interface to carry out parameter estimation and inference. The proposed approach is illustrated by analyzing data collected via self-reports on affective states
Baseline (sea) · Bayesian inference · Bayesian probability · Bivariate analysis · Data mining · Econometrics · Inference · Machine learning · Multilevel model · Ornstein–Uhlenbeck process · Process (computing) · Regression analysis · Statistics · Stochastic process · Variation (astronomy) · Artificial Intelligence · Behavioral Health and Interventions · Computer Science · Innovation Diffusion and Forecasting · Mathematics · Mental Health Research Topics
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| Unique citing works | 15 |
|---|---|
| Citations per year | 1,67 |
| Citation span | 2017 - 2025 (9) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 15 |