On inference assessment in generalized linear models
type: Item
instance of: scholarly article
title: On inference assessment in generalized linear models
zbMATH Open document ID: 0935.62084
author: Andy H. Lee
author: Yer Van Hui
published in: Computational Statistics
publication date: 2000-03-02T00:00:00Z
review text: A generalized linear model is considered in which the observation is a vector of independent random variables \(y_i\) from an exponential family with means \(E(y_i)=\mu_i.\) The vector \(\mu\) satisfies the relation \(g(\mu)=X\beta\), where \(g\) is a known link function, \(X\) is a known matrix and \(\beta\) is an unknown parameter. The authors propose a diagnostic algorithm to assess the outliers and influential observations in this model. The algorithm uses a diagnostic measure based on cross-validation and a bootstrap procedure to determine significance of the value of this measure for each observation. Results of food-stamp data analysis are considered as an example.
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62J20
Mathematics Subject Classification ID: 62G35
zbMATH DE Number: 1409680
zbMATH Keywords: outliers diagnostics
zbMATH Keywords: robust estimation
zbMATH Keywords: bootstrap
zbMATH Keywords: cross-validation
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1409680