Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.12/1693
Título: Consistency and efficiency of ordinary least squares, maximum likelihood, and three type II linear regression models: A Monte-Carlo simulation study
Autor: Maroco, João
Palavras-chave: Measurement errors in predictors
Errors-in-variables
Type II regression
Regression analysis
Data: 2007
Editora: Hogrefe & Huber Publishers
Citação: Methodology, 3(2), 81-88
Resumo: Type I linear regression models, which allow for measurement errors only in the criterion variable, are frequently used in modeling research in psychology and the social sciences. Although there are frequently measurement errors and large natural variation both in the criterion and predictor variables, type II regression methods that account for these errors are seldom used in these fields of study. The consistency and efficiency of three type II regression methods (reduced major axis, Kendall’s robust line-fit and Bartlett’s three-group) were evaluated in comparison to ordinary least squares (OLS) and the maximum likelihood with known variance ratio used frequently in biometrics and econometrics. When predictors are measured with error, OLS slope estimates are biased toward zero, and the same bias was observed with both Kendall’s and Bartlett’s methods. Reduced major axis produced consistent estimates even for small sample sizes, whenever the measurement errors in X are similar in magnitude to measurement errors in Y, but there was a consistent bias when the measurement error in X was smaller/greater than in Y. Maximum likelihood estimates behaved erroneously for small sample sizes, but for larger sample sizes they converged to the expected values.
Peer review: yes
URI: http://hdl.handle.net/10400.12/1693
ISSN: 1614-1881
Aparece nas colecções:MTI - Actas de reuniões cientificas

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