
Multivariate boundary regression models
In this work, we consider a nonparametric regression model with oneside...
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Estimation in a simple linear regression model with measurement error
This paper deals with the problem of estimating a slope parameter in a s...
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Multivariate Cluster Weighted Models Using Skewed Distributions
Much work has been done in the area of the cluster weighted model (CWM),...
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Estimations of means and variances in a Markov linear model
Multivariate regression models and ANOVA are probably the most frequentl...
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Does Regression Approximate the Influence of the Covariates or Just Measurement Errors? A Model Validity Test
A criterion is proposed for testing hypothesis about the nature of the e...
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Regression with genuinely functional errorsincovariates
Contamination of covariates by measurement error is a classical problem ...
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A General Modeling Framework for Network Autoregressive Processes
The paper develops a general flexible framework for Network Autoregressi...
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Prediction in polynomial errorsinvariables models
A multivariate errorsinvariables (EIV) model with an intercept term, and a polynomial EIV model are considered. Focus is made on a structural homoskedastic case, where vectors of covariates are i.i.d. and measurement errors are i.i.d. as well. The covariates contaminated with errors are normally distributed and the corresponding classical errors are also assumed normal. In both models, it is shown that (inconsistent) ordinary least squares estimators of regression parameters yield an a.s. approximation to the best prediction of response given the values of observable covariates. Thus, not only in the linear EIV, but in the polynomial EIV models as well, consistent estimators of regression parameters are useless in the prediction problem, provided the size and covariance structure of observation errors for the predicted subject do not differ from those in the data used for the model fitting.
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