The comparison study of the model selection criteria on the Tobit regression model based on the bootstrap sample augmentation mechanisms

2020 ◽  
pp. 1-26
Author(s):  
Yue Su ◽  
P. K. Mwanakatwe
Keyword(s):  
Model Selection ◽  
Regression Model ◽  
Selection Criteria ◽  
Bootstrap Sample ◽  
Comparison Study ◽  
Tobit Regression ◽  
Model Selection Criteria ◽  
Model Based ◽  
Tobit Regression Model

scholarly journals Generalized Measures of Divergence in Survival Analysis and Reliability

2010 ◽  
Vol 47 (1) ◽  
pp. 216-234 ◽  
Author(s):  
Filia Vonta ◽  
Alex Karagrigoriou
Keyword(s):  
Survival Analysis ◽  
Model Selection ◽  
Mutual Information ◽  
Selection Criteria ◽  
Cox Model ◽  
Transformation Model ◽  
Model Selection Criteria ◽  
Divergence Measures

Measures of divergence or discrepancy are used either to measure mutual information concerning two variables or to construct model selection criteria. In this paper we focus on divergence measures that are based on a class of measures known as Csiszár's divergence measures. In particular, we propose a measure of divergence between residual lives of two items that have both survived up to some time t as well as a measure of divergence between past lives, both based on Csiszár's class of measures. Furthermore, we derive properties of these measures and provide examples based on the Cox model and frailty or transformation model.

scholarly journals PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION

2015 ◽  
Vol 28 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Shuichi Kawano ◽  
Ibuki Hoshina ◽  
Kaito Shimamura ◽  
Sadanori Konishi
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Selection Criteria ◽  
Lasso Regression ◽  
Bayesian Lasso ◽  
Model Selection Criteria

scholarly journals Using Model Selection Criteria to Choose the Number of Principal Components

2021 ◽  
Vol 20 (3) ◽  
pp. 450-461
Author(s):  
Stanley L. Sclove
Keyword(s):  
Model Selection ◽  
Principal Components ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Information Criteria ◽  
Akaike's Information Criterion ◽  
Model Selection Criteria ◽  
Adequate Number ◽  
Number Of Principal Components

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

scholarly journals Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Regression Model ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Tobit Model ◽  
Severe Injury ◽  
Parameter Estimates ◽  
Random Parameters ◽  
Tobit Regression ◽  
Accident Rates ◽  
Tobit Regression Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

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