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has been cited by the following article:
TITLE: Maximum Entropy Empirical Likelihood Methods Based on Laplace Transforms for Nonnegative Continuous Distribution with Actuarial Applications
AUTHORS: Andrew Luong
KEYWORDS: Quasi-Likelihood, Projection, Power Mixture Operator, Quadratic Distance Methods, Insurance Premium, Stop-Loss Premium
JOURNAL NAME: Open Journal of Statistics, Vol.7 No.3, June 14, 2017
ABSTRACT: Maximum entropy likelihood (MEEL) methods also known as exponential tilted empirical likelihood methods using constraints from model Laplace transforms (LT) are introduced in this paper. An estimate of overall loss of efficiency based on Fourier cosine series expansion of the density function is proposed to quantify the loss of efficiency when using MEEL methods. Penalty function methods are suggested for numerical implementation of the MEEL methods. The methods can easily be adapted to estimate continuous distribution with support on the real line encountered in finance by using constraints based on the model generating function instead of LT.