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    An alternative estimator for regression coefficients with outliers 

    Titirut Mekbunditkul; Pachitjanut Siripanich, advisor (National Institute of Development Administration, 2010)

    An important problem often found in a regression analysis is that a structural change in regression exists and/or the observed data contain outliers. These can lead to a violation of the Gauss-Markov assumptions and affect least squares (LS), rendering the regression inadequate. In this dissertation, an alternative regression model has been constructed from a combination of two principle ideas, namely the Tobit and piecewise regressions. This combined model, called the Tobit-piecewise (TP) regression model, can be suitably applied to cope with ...
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    Estimation of regression coefficients with outliers 

    Pimpan Amphanthong; Prachoom Suwattee, advisor (National Institute of Development Administration, 2009)

    In linear models, the ordinary least squares estimators of have always turned out to be the best linear unbiased estimates. When the sample data contain outliers, the outliers may have a considerable effect on the least-squares estimates of , and an alternative approach to the problem is needed to obtain a better fit of the model or more precise estimates of . In this study, new weights were constructed for the sample data from two new influence functions and applied in the estimation of regression coefficients with outliers. Two sets of ...