|Title||Variable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Stefanski, L A., Yichao Wu, and Kyle White|
|Journal||J Am Stat Assoc|
Using the relationships among ridge regression, LASSO estimation, and measurement error attenuation as motivation, a new measurement-error-model-based approach to variable selection is developed. After describing the approach in the familiar context of linear regression, we apply it to the problem of variable selection in nonparametric classification, resulting in a new kernel-based classifier with LASSO-like shrinkage and variable-selection properties. Finite-sample performance of the new classification method is studied via simulation and real data examples, and consistency of the method is studied theoretically. Supplementary materials for the paper are available online.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Variable selection in nonparametric classification via measurement error model selection likelihoods.|
|PubMed Central ID||PMC4066561|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA085848 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States
T32 HL079896 / HL / NHLBI NIH HHS / United States
Variable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods.