Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression.

Abstract

This document studied optimally bounded score functions for estimating regression parameters in a generalized linear model. This work extends results obtained by Krasker & Welsch (1982) for the linear model and provides a simple proof of Krasker and Welsch's first order condition for strong optimality. The application of these results to logistic regression is studied in some detail with an example given comparing the bounded influence estimator with maximum likelihood. Additional keywords: Outliers; Robustness; Influentral points. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1985
Accession Number
ADA160348

Entities

People

  • D. Ruppert
  • L. A. Stefanski
  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Covariance
  • Efficiency
  • Equations
  • Estimators
  • Maximum Likelihood Estimation
  • North Carolina
  • Observation
  • Residuals
  • Security
  • Sensitivity
  • Standards
  • Statistical Algorithms
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Regression Analysis.
  • Statistical inference.