Piecewise Empirical Likelihood
Abstract
Non-parametric methods avoid the problem of having to specify a particular data generating mechanism, but can be computationally intensive, reducing their accessibility for large data problems. Empirical likelihood, a non-parametric approach to the likelihood function, is also limited in application due to the computational demands necessary. We propose a new approach that combines multiple non-parametric likelihood-type components to build a data-driven approximation of the true function. We will examine the theoretical properties of this piecewise empirical likelihood and demonstrate the computational gains of this methodology.
Cite
@article{arxiv.1604.06383,
title = {Piecewise Empirical Likelihood},
author = {Adam Jaeger and Nicole Lazar},
journal= {arXiv preprint arXiv:1604.06383},
year = {2017}
}
Comments
Significant changes have been made to article, renamed split sample empirical likelihood. Replacement article is titled Split Sample Empirical Likelihood arXiv:1703.03312