A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^

Análisis de la evolución de los resultados de los alumnos y la metodología Bootstrap para detección de cambios

En este trabajo nos enfocamos en el problema del punto de cambio aplicado al control de calidad del proceso enseñanza-aprendizaje. Para ello se tomo en cuenta la evolución temporal de la proporción de alumnos promocionados, por cuatrimestre, de la asignatura Estadística de la Facultad de Ingeniería de la UNLP, desde el año 2001 al 2008. El objetivo es analizar la posible aparición de cambios en dicha proporción no detectados por las cartas de control convencionales. Se trata de establecer las posibles causas de esos cambios en el marco de las transformaciones ocurridas a partir de la acreditación de las carreras de Ingeniería de la UNLP, usando estas herramientas de estudio. El análisis de punto de cambio es una novedosa herramienta utilizada con el fin de determinar la existencia o no de cambios en procesos de diferente índole. Para su aplicación se emplea un test de hipótesis y la metodología Bootstrap.

Análisis de la evolución de los resultados de los alumnos y la metodología Bootstrap para detección de cambios

En este trabajo nos enfocamos en el problema del punto de cambio aplicado al control de calidad del proceso enseñanza-aprendizaje. Para ello se tomo en cuenta la evolución temporal de la proporción de alumnos promocionados, por cuatrimestre, de la asignatura Estadística de la Facultad de Ingeniería de la UNLP, desde el año 2001 al 2008. El objetivo es analizar la posible aparición de cambios en dicha proporción no detectados por las cartas de control convencionales. Se trata de establecer las posibles causas de esos cambios en el marco de las transformaciones ocurridas a partir de la acreditación de las carreras de Ingeniería de la UNLP, usando estas herramientas de estudio. El análisis de punto de cambio es una novedosa herramienta utilizada con el fin de determinar la existencia o no de cambios en procesos de diferente índole. Para su aplicación se emplea un test de hipótesis y la metodología Bootstrap.