Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data

DOI10.1007/S11336-019-09664-7zbMath1431.62549OpenAlexW2921084597WikidataQ92288506 ScholiaQ92288506MaRDI QIDQ2331187

Nilam Ram, Sy-Miin Chow, Pamela M. Cole, Zhao-hua Lu

Publication date: 25 October 2019

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6844193

zbMATH Keywords

regime switchingstochastic differential equationsBayesian methodsOrnstein-UhlenbeckMarkov chain Monte Carlo algorithmsMarkov switching transition

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Hypothesis testing in multivariate analysis (62H15) Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Markov processes: hypothesis testing (62M02) Applications of statistics to psychology (62P15)

Related Items (2)

Bayesian forecasting with a regime-switching zero-inflated multilevel Poisson regression model: an application to adolescent alcohol use with spatial covariates ⋮ Forecasting intra-individual changes of affective states taking into account inter-individual differences using intensive longitudinal data from a university student dropout study in math

Uses Software

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• BaM
• JAGS
• PROC LTA

Cites Work

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• Using state-space model with regime switching to represent the dynamics of facial electromyography (EMG) data
• Fitting nonlinear ordinary differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm
• A regularized bridge sampler for sparsely sampled diffusions
• Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data
• Monte Carlo maximum likelihood estimation for discretely observed diffusion processes
• Bayesian inference for nonlinear multivariate diffusion models observed with error
• Slice sampling. (With discussions and rejoinder)
• Continuous time state space modeling of panel data by means of sem
• Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering
• Erratum to: ``Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
• Closed-form likelihood expansions for multivariate diffusions
• Nonlinear regime-switching state-space (RSSS) models
• On inference for partially observed nonlinear diffusion models using the Metropolis-Hastings algorithm
• SEM Modeling with Singular Moment Matrices Part II: ML-Estimation of Sampled Stochastic Differential Equations
• SEM Modeling with Singular Moment Matrices Part I: ML-Estimation of Time Series
• Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
• Continuous time modeling of panel data: SEM versus filter techniques
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The aliasing‐phenomenon in visual terms
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Adaptive Rejection Metropolis Sampling within Gibbs Sampling
• Likelihood Inference for Discretely Observed Nonlinear Diffusions
• Analytic value function for optimal regime-switching pairs trading rules
• Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
• Bayesian Measures of Model Complexity and Fit
• Bayes Factors
• Parameter Estimation for Differential Equations: a Generalized Smoothing Approach
• Monte Carlo sampling methods using Markov chains and their applications
• Model-Based Clustering, Classification, and Density Estimation Using mclust in R

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