Online Program Home
  My Program

Abstract Details

Activity Number: 474 - Nonparametric Density and Variance Estimation
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #322960 View Presentation
Title: Rank Based Estimation for Double Generalized Linear Model
Author(s): Brice Merlin Nguelifack* and Guy-vanie M. Miakonkana and Eddy Kwessi
Companies: United States Naval Academy and African School of Economics and Trinity University
Keywords: Double Generalized Linear Model ; Consistency ; Asymptotic Normality ; Wilcoxon ; Robustness

In this paper, we consider the estimation of parameters of a double generalized linear model in which both the mean and the variance are allowed to depend on explanatory variables. The proposed estimation technique is conducted in a two-step process, iterating between the estimation of the mean and that of the variance by minimizing a rank based objective function, similar to the one in Miakonkana et al (2014), at each step. This extends the estimation method proposed in Miakonkana et al (2014) to generalized linear models with unknown variance structure, yielding more flexibility in the model. Unlike the maximum likelihood based double generalized linear models, the proposed estimator is robust to outliers in the response. In addition the proposed estimator inherits theoretical properties, that is, consistency and asymptotic normality, of the estimators developed in Miakonkana et al (2014). It is shown through a simulation study and a real world data example that the rank based estimator is more robust than the maximum likelihood based estimators, in the sense of being less sensitive to perturbations in the response of either the mean or the variance model.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association