Stochastic Regression with Errors in Both Variables
作者:
ReilmanMiriam A.,
GunstRichard F.,
LakshminarayananMani Y.,
期刊:
Journal of Quality Technology
(Taylor Available online 1986)
卷期:
Volume 18,
issue 3
页码: 162-169
ISSN:0022-4065
年代: 1986
DOI:10.1080/00224065.1986.11979004
出版商: Taylor&Francis
关键词: Calibration;Least Squares Regression;Maximum Likelihood;Measurement Error
数据来源: Taylor
摘要:
Linear structural models are linear relationships between two stochastic (random) variates in which both of the variates are subject to measurement errors. Structural models are common in experimental work, but are typically fit using least squares. In this expository paper maximum likelihood estimators for linear structural models are presented and contrasted with the corresponding least squares estimators. Asymptotic variance formulae for the intercept and slope estimators are given, along with the corresponding expressions for linear functional models.
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