Kernel Binary Regression with Multiple Covariates

Multiple functional regression with both discrete and continuous covariates

In this paper we present a nonparametric method for extending functional regression methodology to the situation where more than one functional covariate is used to predict a functional response. Borrowing the idea from Kadri et al. (2010a), the method, which support mixed discrete and continuous explanatory variables, is based on estimating a function-valued function in reproducing kernel Hilb...

Multiple Imputation for M-Regression With Censored Covariates

Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints

Nonparametric smoothing under shape constraints has recently received much well-deserved attention. Powerful methods have been proposed for imposing a single shape constraint such as monotonicity and concavity on univariate functions. In this paper, we extend the monotone kernel regression method in Hall and Huang (2001) to the multivariate and multi-constraint setting. We impose equality and/o...

Regression Discontinuity Design with Covariates

Regression Discontinuity Design with Covariates In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd, and...

Multiple Kernel Learning for Support Vector Regression ∗

Kernel support vector (SV) regression has successfully been used for prediction of nonlinear and complicated data. However, like other kernel methods such as support vector machine (SVM) classification, the quality of SV regression depends on proper choice of kernel functions and their parameters. Kernel selection for model selection is conventionally performed through repeated cross validation...