Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers

<table>The average effect of outliers on standard errors of parameters for sample size <svg height="11.125" id="M116" style="vertical-align:-0.1638pt" version="1.1" viewbox="0 0 52.5625 11.125" width="52.5625" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,10.862)"><use xlink:href="#x1D45B"></use></g><g transform="matrix(.017,-0,0,-.017,13.305,10.862)"><use xlink:href="#x3D"></use></g><g transform="matrix(.017,-0,0,-.017,28.008,10.862)"><path d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" id="x31"></path></g><g transform="matrix(.017,-0,0,-.017,36.168,10.862)"><use xlink:href="#x30"></use></g><g transform="matrix(.017,-0,0,-.017,44.327,10.862)"><use xlink:href="#x30"></use></g>
</svg>.</table>

Mathematical Problems in Engineering

fig4

Figure 4

Figure 4: Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers 