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- W30367290 abstract "We discuss some methods of estimation in bivariate errors-in- variables linear models. We also suggest a method of constructing consistent estimators in the case when the error disturbances have the normal distri- bution with unknown parameters. It is based on the theory of estimating variance components in linear models. A simulation study is presented which compares this estimator with the maximum likelihood one. 1. Introduction. Simple linear regression models describe linear func- tional relationships when there is an observation error in only the depen- dent variable Y. The X (independent variable) is assumed to be mea- sured precisely. The parameters in this model are estimated by the clas- sical ordinary least squares (OLS) method, which gives unbiased estima- tors. In situations where both variables are subject to error the errors-in- variables models are applied. We assume that the true linear relationship is given by y = as+b. The actual observed values are X = s+ and Y = y+ , where the symbols and denote the corresponding error disturbances. The measurement error in X sometimes happens to be overlooked and the OLS estimation of the parameters is chosen because of its familiarity and ease of use. It is well known that in the errors-in-variables model, the simple regression estimator (OLS) is inconsistent. Cochran (1968) has given a general discussion of the consequences of using the OLS estimator in errors-in-variables models. However, there exist cases when this estimator has mean squared error smaller than other esti-" @default.
- W30367290 created "2016-06-24" @default.
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- W30367290 date "1999-01-01" @default.
- W30367290 modified "2023-09-26" @default.
- W30367290 title "On estimation of parameters in the bivariate linear errors-in-variables model" @default.
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- W30367290 doi "https://doi.org/10.4064/am-25-4-401-410" @default.
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