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arXiv · 1604.06383

Piecewise Empirical Likelihood

Adam Jaeger·Nicole Lazar
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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.

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Adam Jaeger, Nicole Lazar. 2016-04-21. Piecewise Empirical Likelihood. https://arxiv.org/abs/1604.06383

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