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Source: Journal of Applied Statistics
Resulting in 3 citations.
1. Cai, Jingheng
Liang, Zhibin
Sun, Rongqian
Liang, Chenyi
Pan, Junhao
Bayesian Analysis of Latent Markov Models with Non-ignorable Missing Data
Journal of Applied Statistics published online (27 February 2019): DOI: 10.1080/02664763.2019.1584162.
Also: https://www.tandfonline.com/doi/full/10.1080/02664763.2019.1584162
Cohort(s): NLSY97
Publisher: Taylor & Francis Group
Keyword(s): Bayesian; Household Income; Modeling; Poverty

Permission to reprint the abstract has not been received from the publisher.

Latent Markov models (LMMs) are widely used in the analysis of heterogeneous longitudinal data. However, most existing LMMs are developed in fully observed data without missing entries. The main objective of this study is to develop a Bayesian approach for analyzing the LMMs with non-ignorable missing data. Bayesian methods for estimation and model comparison are discussed. The empirical performance of the proposed methodology is evaluated through simulation studies. An application to a data set derived from National Longitudinal Survey of Youth 1997 is presented.
Bibliography Citation
Cai, Jingheng, Zhibin Liang, Rongqian Sun, Chenyi Liang and Junhao Pan. "Bayesian Analysis of Latent Markov Models with Non-ignorable Missing Data." Journal of Applied Statistics published online (27 February 2019): DOI: 10.1080/02664763.2019.1584162.
2. Zhang, Zhiyong
McArdle, John J.
Nesselroade, John R.
Growth Rate Models: Emphasizing Growth Rate Analysis through Growth Curve Modeling
Journal of Applied Statistics 39,6 (June 2012): 1241-1262.
Also: http://www.tandfonline.com/doi/abs/10.1080/02664763.2011.644528
Cohort(s): Children of the NLSY79
Publisher: Taylor & Francis Group
Keyword(s): Behavior Problems Index (BPI); Gender Differences; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Math); Test Scores/Test theory/IRT

Permission to reprint the abstract has not been received from the publisher.

To emphasize growth rate analysis, we develop a general method to reparametrize growth curve models to analyze rates of growth for a variety of growth trajectories, such as quadratic and exponential growth. The resulting growth rate models are shown to be related to rotations of growth curves. Estimated conveniently through growth curve modeling techniques, growth rate models have advantages above and beyond traditional growth curve models. The proposed growth rate models are used to analyze longitudinal data from the National Longitudinal Study of Youth (NLSY) on children's mathematics performance scores including covariates of gender and behavioral problems (BPI). Individual differences are found in rates of growth from ages 6 to 11. Associations with BPI, gender, and their interaction to rates of growth are found to vary with age. Implications of the models and the findings are discussed.
Bibliography Citation
Zhang, Zhiyong, John J. McArdle and John R. Nesselroade. "Growth Rate Models: Emphasizing Growth Rate Analysis through Growth Curve Modeling." Journal of Applied Statistics 39,6 (June 2012): 1241-1262.
3. Zimmer, David M.
The Heterogeneous Impact of Insurance on Health Care Demand among Young Adults: A Panel Data Analysis
Journal of Applied Statistics 45,7 (2018): 1277-1291.
Also: https://www.tandfonline.com/doi/abstract/10.1080/02664763.2017.1369497
Cohort(s): NLSY97
Publisher: Taylor & Francis Group
Keyword(s): Health Care; Insurance, Health; Legislation; Medical Expenditure Panel Survey (MEPS)

Permission to reprint the abstract has not been received from the publisher.

Success of the recently implemented Affordable Care Act hinges on previously uninsured young adults enrolling in coverage. How will increased coverage, in turn, affect health care utilization? This paper applies variable coefficient panel models to estimate the impact of insurance on health care utilization among young adults. The econometric setup, which accommodates nonlinear usage measures, attempts to address the potential endogeneity of insurance status. The main finding is that, for approximately one-fifth of young adults, insurance does not substantially alter health care consumption. On the other hand, another one-fifth of young adults have large moral hazard effects. Among that group, insurance increases the probability of having a routine checkup by 71-120%, relative to mean probabilities, and insurance increases the number of curative-based doctor office visits by 67-181%, relative to the mean number of visits.
Bibliography Citation
Zimmer, David M. "The Heterogeneous Impact of Insurance on Health Care Demand among Young Adults: A Panel Data Analysis." Journal of Applied Statistics 45,7 (2018): 1277-1291.