Resistant Dimension Reduction
Existing DR methods such as ordinary least squares and sliced inverse regression often perform poorly in the presence of outliers. Also the DR theory usually assumes that the predictors satisfy the condition of linearly related predictors: e.g., for 1D regression E[x|beta' x] must be a linear function of beta'x. This dissertation develops outlier resistant DR methods that can give useful results when the assumption of linearly related predictors is violated.F 60 .044 .028 .024 .026 .025 .064 .202 .194 .238 .265 .187 x2 .382 .101 .059 . 043 .042 .087 .231 .233 .261 .286 .214 ... F 60 .045 .021 .026 .028 .039 .057 .154 .192 .135 .098 .032 x2 .382 .089 .050 .050 .060 .075 .182 .221 .150 .114 .050anbsp;...
| Title | : | Resistant Dimension Reduction |
| Author | : | Jing Chang |
| Publisher | : | ProQuest - 2006 |
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