17 citations · 29 across the 10 of their papers we have counts for
7 papers · 1 filter
Bagging cross-validated bandwidths with application to Big Data
Daniel Barreiro-Ures, Ricardo Cao, Mario Francisco Fernández +1
Hall and Robinson (2009) proposed and analyzed the use of bagged cross-validation to choose the bandwidth of a kernel density estimator. They established that bagging greatly reduc…
Screening Methods for Classification Based on Non-parametric Bayesian Tests
Naveed Merchant, Jeffrey D. Hart
Feature or variable selection is a problem inherent to large data sets. While many methods have been proposed to deal with this problem, some can scale poorly with the number of pr…
Estimating the Mean and Variance of a High-dimensional Normal Distribution Using a Mixture Prior
Shyamalendu Sinha, Jeffrey D. Hart
This paper provides a framework for estimating the mean and variance of a high-dimensional normal density. The main setting considered is a fixed number of vector following a high-…
Partitioned Cross-Validation for Divide-and-Conquer Density Estimation
Anirban Bhattacharya, Jeffrey D. Hart
We present an efficient method to estimate cross-validation bandwidth parameters for kernel density estimation in very large datasets where ordinary cross-validation is rendered hi…
Theoretical Properties and Practical Performance of Fully Robust One-Sided Cross-Validation
Olga Y. Savchuk, Jeffrey D. Hart
Fully robust OSCV is a modification of the OSCV method that produces consistent bandwidth in the cases of smooth and nonsmooth regression functions. The current implementation of t…
Empirical study of indirect cross-validation
Olga Y. Savchuk, Jeffrey D. Hart, Simon J. Sheather
In this paper we provide insight into the empirical properties of indirect cross-validation (ICV), a new method of bandwidth selection for kernel density estimators. First, we desc…