4 research outputs found
Asymptotic properties of minimum distance estimators dependent on covariables.
En este trabajo se obtienen propiedades de consistencia y normalidad asint贸tica para el estimador no param茅trico de la funci贸n de regresi贸n (m(x)) resultante de la extensi贸n de la metodolog铆a de m铆nima distancia de Cramer-von Mises al contexto de la estimaci贸n de curvas. Se hacen algunas consideraciones acerca de la robustez del estimador resultante en base a la funci贸n de influencia local (LIF) y se realiza un estudio de Monte Carlo comparativo con otros m茅todos de estimaci贸n.In this paper consistency and asymptotic normality are obtained for a class of nonparametric regression function estimators arising from the extension of the minimum Cramer-von Mises distance methodology to the context of curve estimation. Some considerations about the robustness of these estimators are made, based on the concept of local influence function (LIF). Also, we present a Monte Carlo study for the comparison between these estimators and other methods of estimation
Asymptotic properties of minimum distance estimators dependent on covariables.
En este trabajo se obtienen propiedades de consistencia y normalidad asint贸tica para el estimador no param茅trico de la funci贸n de regresi贸n (m(x)) resultante de la extensi贸n de la metodolog铆a de m铆nima distancia de Cramer-von Mises al contexto de la estimaci贸n de curvas. Se hacen algunas consideraciones acerca de la robustez del estimador resultante en base a la funci贸n de influencia local (LIF) y se realiza un estudio de Monte Carlo comparativo con otros m茅todos de estimaci贸n.In this paper consistency and asymptotic normality are obtained for a class of nonparametric regression function estimators arising from the extension of the minimum Cramer-von Mises distance methodology to the context of curve estimation. Some considerations about the robustness of these estimators are made, based on the concept of local influence function (LIF). Also, we present a Monte Carlo study for the comparison between these estimators and other methods of estimation
Overall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman
Abstract. We propose a new application of the Friedman statistical test of significance to compare multiple retrieval methods. After measuring the average precision at the eleven standard levels of recall, our application of the Friedman test provides a global comparison of the methods. In some experiments this test provides additional and useful information to decide if methods are different.