This thesis discusses linear EV (errors-in-variables) regression models, that is, regression models with measurement errors. Because in practice, data are often obtained with measurement errors, EV model is more fit for application than the ordinary regression model. However, it is more complicated in the statistical inference and analysis, so research about this theory is very difficult. Due to the application of statistics, when the weight function uses real variables in EV model, we extend the consistency of the weighted sum for the independent random variable sequence and obtain a result of convergence about the weighted sum for the exchangeable random variable sequence in EV model.
Errors-in-variables Model, The Weight Function Contains Real Variables, Convergence about the Weighted Sum.
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