|Title||Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||White, Kyle R., Leonard A. Stefanski, and Yichao Wu|
|Journal||J Am Stat Assoc|
This paper develops a nonparametric shrinkage and selection estimator via the measurement error selection likelihood approach recently proposed by Stefanski, Wu, and White. The Measurement Error Kernel Regression Operator (MEKRO) has the same form as the Nadaraya-Watson kernel estimator, but optimizes a measurement error model selection likelihood to estimate the kernel bandwidths. Much like LASSO or COSSO solution paths, MEKRO results in solution paths depending on a tuning parameter that controls shrinkage and selection via a bound on the harmonic mean of the pseudo-measurement error standard deviations. We use small-sample-corrected AIC to select the tuning parameter. Large-sample properties of MEKRO are studied and small-sample properties are explored via Monte Carlo experiments and applications to data.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Variable selection in kernel regression using measurement error selection likelihoods.|
|PubMed Central ID||PMC5881957|
|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 Kernel Regression Using Measurement Error Selection Likelihoods.