Covariance Analysis in Generalized Linear Measurement Error Models

Abstract

This summarizes some of the recent work on the errors-in-variables problem in generalized linear models. The focus is on covariance analysis, and in particular testing for and estimation of treatment effects. There is a considerable difference between the randomized and nonrandomized models when testing for an effect. For estimation, one is largely reduced to using an errors in variables analysis. Some of the possible methods are outlined and compared. Keywords: Simple regression models.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1988
Accession Number
ADA197661

Entities

People

  • Raymond J. Carroll

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Air Force
  • Calibration
  • Covariance
  • Data Science
  • Data Sets
  • Distribution Functions
  • Estimators
  • Experimental Design
  • Information Science
  • Knowledge Management
  • Measurement
  • Method Of Moments
  • New York
  • Standards
  • Statistical Algorithms
  • Statistics
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Systems Analysis and Design