Likelihood Inference for Linear Regression Models

Abstract

Approximate conditional inference based on large-sample likelihood ratio methods is considered for the parameters of linear regression models. Mean and variance adjustments that improve the standard normal approximation to the conditional distribution of the signed square root of the log likelihood ratio statistic for a scalar parameter of interest are given. A Bartlett adjustment factor that improves the chi-squared approximation to the conditional distribution of the log likelihood ratio statistic for a vector parameter of interest is also presented. The accuracy of approximate confidence limits obtained by using the adjustments is demonstrated for a location-scale analysis of Darwin's data.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1987
Accession Number
ADA594293

Entities

People

  • T. J. Diciccio

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Analysis Of Variance
  • Confidence Limits
  • Data Science
  • Errors
  • Estimators
  • Information Science
  • Intervals
  • Military Research
  • Models
  • Random Variables
  • Scale Models
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms