TY - JOUR
T1 - Split sample empirical likelihood
AU - Jaeger, Adam
AU - Lazar, Nicole A.
N1 - Funding Information:
This material was based upon work partially supported by the National Science Foundation, United States of America under Grant DMS-1127914 to the Statistical and Applied Mathematical Sciences Institute. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
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U2 - 10.1016/j.csda.2020.106994
DO - 10.1016/j.csda.2020.106994
M3 - Article
AN - SCOPUS:85084935549
VL - 150
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
M1 - 106994
ER -