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- W2081130604 abstract "Consider the linear regression model Y = Xθ+ e where Y denotes a vector of n observations on the dependent variable, X is a known matrix, θ is a vector of parameters to be estimated and e is a random vector of uncorrelated errors. If X'X is nearly singular, that is if the smallest characteristic root of X'X s small then a small perurbation in the elements of X, such as due to measurement errors, induces considerable variation in the least squares estimate of θ. In this paper we examine for the asymptotic case when n is large the effect of perturbation with regard to the bias and mean squared error of the estimate." @default.
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- W2081130604 date "1980-01-01" @default.
- W2081130604 modified "2023-09-22" @default.
- W2081130604 title "Measurement error in regression analysis" @default.
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- W2081130604 doi "https://doi.org/10.1080/03610928008827915" @default.
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