On Selecting the Strongest Linear Relationship Between a Response Variable and an Explanatory Variable in Measurement Error Models

Abstract

Measurement errors are the differences between the actual desired values and the observed values. In the real world, it is usually very difficult to obtain exactly the "true" values. Instead, one may only get the observed values that are related to the true values through the measurement errors. In this paper we investigate the problem of selecting the treatment that has the strongest relationship between the response variable and an explanatory variable in a linear measurement error model. A selection procedure based on moment estimates has been developed and the large sample performance of the derived selection rule has also been analyzed. At the end of this paper, a simulation study is carried out to illustrate the large sample performance of the selection procedure.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2000
Accession Number
ADA384447

Entities

People

  • Shanti Gupta
  • Xun Lin

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Convergence
  • Data Science
  • Estimators
  • Experimental Design
  • Information Science
  • Iterations
  • Measurement
  • Military Research
  • New York
  • Nonlinear Dynamics
  • Normal Distribution
  • Probability
  • Random Variables
  • Regression Analysis
  • Simulations
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.