Empirical likelihood offers a nonparametric approach to estimation and inference, which replaces the probability density-based likelihood function with a function defined by estimating equations. While this eliminates the need for a parametric specification, the restriction of numerical optimization greatly decreases the applicability of empirical likelihood for large data problems. A solution to this problem is the split sample empirical likelihood; this variant utilizes a divide and conquer approach, allowing for parallel computation of the empirical likelihood function. The results show the asymptotic distribution of the estimators and test statistics derived from the split sample empirical likelihood are the same seen in standard empirical likelihood yet have significantly decreased computational times.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics