Piecewise Empirical Likelihood
  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. 
Adam Jaeger and Nicole Lazar
stat.CO