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Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates

Author

Listed:
  • Yanling Li

    (The Pennsylvania State University)

  • Zita Oravecz

    (The Pennsylvania State University)

  • Shuai Zhou

    (The Pennsylvania State University)

  • Yosef Bodovski

    (The Pennsylvania State University)

  • Ian J. Barnett

    (University of Pennsylvania)

  • Guangqing Chi

    (The Pennsylvania State University)

  • Yuan Zhou

    (University of Minnesota)

  • Naomi P. Friedman

    (University of Colorado Boulder)

  • Scott I. Vrieze

    (University of Minnesota)

  • Sy-Miin Chow

    (The Pennsylvania State University)

Abstract

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals’ data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals’ log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Suggested Citation

  • Yanling Li & Zita Oravecz & Shuai Zhou & Yosef Bodovski & Ian J. Barnett & Guangqing Chi & Yuan Zhou & Naomi P. Friedman & Scott I. Vrieze & Sy-Miin Chow, 2022. "Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 376-402, June.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:2:d:10.1007_s11336-021-09831-9
    DOI: 10.1007/s11336-021-09831-9
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    References listed on IDEAS

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    1. Peter F. Halpin & Kathleen Gates & Siwei Liu, 2022. "Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 373-375, June.

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