Bootstrapping Likelihood for Model Selection with Small Samples Export

@misc{citeulike:3753888, abstract = {this report we compare model-selection performance of AIC, EIC, a bootstrap-smoothed likelihood crossvalidation (BCV) and its modification (632CV) in small-sample linear regression, logistic regression and Cox regression. Simulation results show that EIC largely overcomes AIC\&\#039;s over-fitting problem and that BCV may be better than EIC. Hence, the three methods based on bootstrapping the likelihood establish themselves as important alternatives to AIC in model selection with small samples. Key words: AIC; Cox regression; Cross-validation; EIC; Linear regression; Logistic regression; Maximum likelihood. 1. INTRODUCTION}, author = {Pan, Wei}, citeulike-article-id = {3753888}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.7923}, keywords = {aic, stats}, posted-at = {2008-12-08 01:14:27}, priority = {2}, title = {Bootstrapping Likelihood for Model Selection with Small Samples}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.7923}, year = {1998} }

TY - ELEC ID - citeulike:3753888 L3 - citeulike-article-id:3753888 N2 - this report we compare model-selection performance of AIC, EIC, a bootstrap-smoothed likelihood crossvalidation (BCV) and its modification (632CV) in small-sample linear regression, logistic regression and Cox regression. Simulation results show that EIC largely overcomes AIC's over-fitting problem and that BCV may be better than EIC. Hence, the three methods based on bootstrapping the likelihood establish themselves as important alternatives to AIC in model selection with small samples. Key words: AIC; Cox regression; Cross-validation; EIC; Linear regression; Logistic regression; Maximum likelihood. 1. INTRODUCTION TI - Bootstrapping Likelihood for Model Selection with Small Samples KW - aic KW - stats AU - Pan, Wei PY - 1998/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.7923 ER -