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Full-Text Articles in Statistics and Probability
Joint Models Of Longitudinal Outcomes And Informative Time, Jangdong Seo
Joint Models Of Longitudinal Outcomes And Informative Time, Jangdong Seo
Journal of Modern Applied Statistical Methods
Longitudinal data analyses commonly assume that time intervals are predetermined and have no information regarding the outcomes. However, there might be irregular time intervals and informative time. Presented are joint models and asymptotic behaviors of the parameter estimates. Also, the models are applied for real data sets.
The Information Criterion, Masume Ghahramani
The Information Criterion, Masume Ghahramani
Journal of Modern Applied Statistical Methods
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the estimator of asymptotic unbias for the second term Kullbake-Leibler risk considers the divergence between the true model and offered models. However, it is an inconsistent estimator. A proposed approach the problem is the use of A'IC, a consistently offered information criterion. Model selection of classic and linear models are considered by a Monte Carlo simulation.
A Comparative Study Of Bayesian Model Selection Criteria For Capture-Recapture Models For Closed Populations, Ross M. Gosky, Sujit K. Ghosh
A Comparative Study Of Bayesian Model Selection Criteria For Capture-Recapture Models For Closed Populations, Ross M. Gosky, Sujit K. Ghosh
Journal of Modern Applied Statistical Methods
Capture-Recapture models estimate unknown population sizes. Eight standard closed population models exist, allowing for time, behavioral, and heterogeneity effects. Bayesian versions of these models are presented and use of Akaike's Information Criterion (AIC) and the Deviance Information Criterion (DIC) are explored as model selection tools, through simulation and real dataset analysis.
Practical Unit-Root Analysis Using Information Criteria: Simulation Evidence, Kosei Fukuda
Practical Unit-Root Analysis Using Information Criteria: Simulation Evidence, Kosei Fukuda
Journal of Modern Applied Statistical Methods
The information-criterion-based model selection method for detecting a unit root is proposed. The simulation results suggest that the performances of the proposed method are usually comparable to and sometimes better than those of the conventional unit-root tests. The advantages of the proposed method in practical applications are also discussed.
Model-Selection-Based Monitoring Of Structural Change, Kosei Fukuda
Model-Selection-Based Monitoring Of Structural Change, Kosei Fukuda
Journal of Modern Applied Statistical Methods
Monitoring structural change is performed not by hypothesis testing but by model selection using a modified Bayesian information criterion. It is found that concerning detection accuracy and detection speed, the proposed method shows better performance than the hypothesis-testing method. Two advantages of the proposed method are also discussed.