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Efficient estimation for an accelerated failure time model with a cure fraction
 Statist. Sinica
, 2010
"... Abstract: We study the accelerated failure time model with a cure fraction via kernelbased nonparametric maximum likelihood estimation. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error density, in which a kernelsmoothed conditional p ..."
Abstract

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Abstract: We study the accelerated failure time model with a cure fraction via kernelbased nonparametric maximum likelihood estimation. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error density, in which a kernelsmoothed conditional profile likelihood is maximized in the Mstep. We show that with a proper choice of the kernel bandwidth parameter, the resulting estimates are consistent and asymptotically normal. The asymptotic covariance matrix can be consistently estimated by inverting the empirical Fisher information matrix obtained from the profile likelihood using the EM algorithm. Numerical examples are used to illustrate the finitesample performance of the proposed estimates.
RISE RICE INITIATIVE for theSTUDY of ECONOMICS Banking Crises, Early Warning Models, and E ¢ ciency
, 2015
"... This paper proposes a general model that combines the Mixture Hazard Model with the Stochastic Frontier Model for the purposes of investigating the main determinants of the failures and performances of a panel of U.S. commercial banks during the
nancial crisis that began in 2007. The combined model ..."
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This paper proposes a general model that combines the Mixture Hazard Model with the Stochastic Frontier Model for the purposes of investigating the main determinants of the failures and performances of a panel of U.S. commercial banks during the
nancial crisis that began in 2007. The combined model provides measures of the probability and time to failure conditional on a banks performance and vice versa. Both continuoustime and discretetime speci
cations of the model are considered in the paper. The estimation is carried out via the expectationmaximization algorithm due to incomplete information regarding the identity of atrisk banks. In and outofsample predictive accuracy of the proposed models is investigated in order to assess their potential to serve as early warning tools. JEL classi
cation codes: C33, C41, C51, D24, G01, G21. Key words and phrases: Financial distress, panel data, bank failures, semiparametric mixture hazard model, discretetime mixture hazard model, bank e ¢ ciency.