Estimating hospital production functions through flexible regression models

Estimating hospital production functions through flexible regression models

The subject of discussion is health care production functions. The parametric two-factor Cobb–Douglas and transcendental logarithmic (translog) production functions are frequently used. However, empirical and theoretical work has often questioned the validity of the parametric Cobb–Douglas and translog as a model for the production of health care. The aim of this study is to propose a new flexi...

Estimating Hospital Services Production Functions through Flexible Regression Models

a Departamento de Organización de Empresas y Mercadotecnia. University of Vigo, Spain b Instituto Universitario de Ciencias Neurológicas. University of Santiago de Compostela, Spain c Unit of Biostatistics, Dept. of Statistics and Operations Research. University of Santiago de Compostela, Spain d Complexo Hospitalario Universitario de Santiago de Compostela (CHUS). Santiago de Compostela, Spain...

Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models.

We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pert...

A Flexible Method of Estimating Luminosity Functions

We describe a Bayesian approach to estimating luminosity functions. We derive the likelihood function and posterior probability distribution for the luminosity function, given the observed data, and we compare the Bayesian approach with maximum-likelihood by simulating sources from a Schechter function. For our simulations confidence intervals derived from bootstrapping the maximum-likelihood e...

Flexible Regression Models and Relative Forecast Performance ∗