Piecewise Empirical Likelihood

2016/04/21 by Jaeger, Adam, Lazar, Nicole
#Computation (stat.CO) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1604.06383

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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