Rodeo: Sparse, greedy nonparametric regression
نویسندگان
چکیده
منابع مشابه
Nonparametric Density Estimation in High Dimensions Using the Rodeo
We consider the problem of estimating the joint density of a d-dimensional random vector X = (X1, X2, ..., Xd) when d is large. We assume that the density is a product of a parametric baseline component and a nonparametric component. The nonparametric component depends on an unknown subset of the variables. If this subset is small, then nonparametric estimates with fast rates of convergence are...
متن کاملSparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
We consider the problem of estimating the joint density of a d-dimensional random vector X = (X1,X2, ...,Xd) when d is large. We assume that the density is a product of a parametric component and a nonparametric component which depends on an unknown subset of the variables. Using a modification of a recently developed nonparametric regression framework called rodeo (regularization of derivative...
متن کاملRodeo: Sparse Nonparametric Regression in High Dimensions
We present a method for nonparametric regression that performs bandwidth selection and variable selection simultaneously. The approach is based on the technique of incrementally decreasing the bandwidth in directions where the gradient of the estimator with respect to bandwidth is large. When the unknown function satisfies a sparsity condition, our approach avoids the curse of dimensionality, a...
متن کاملNonparametric Greedy Algorithms for the Sparse Learning Problem
This paper studies the forward greedy strategy in sparse nonparametric regression. For additive models, we propose an algorithm called additive forward regression; for general multivariate models, we propose an algorithm called generalized forward regression. Both algorithms simultaneously conduct estimation and variable selection in nonparametric settings for the high dimensional sparse learni...
متن کاملMultivariate Dyadic Regression Trees for Sparse Learning Problems
We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs). Unlike traditional dyadic decision trees (DDTs) or classification and regression trees (CARTs), MDRTs are constructed using penalized empirical risk minimization with a novel sparsity-inducing penalty. Theoretically, we show that MDRTs can simultaneously adapt to the unknown sparsity and smooth...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015