  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.

Computation