Empirical Likelihood

25 Nov 2024 10:40

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Yet Another Inadequate Placeholder

This is an extremely clever idea for retaining many of the conveniences of ordinary likelihood-based statistical methods, when the assumptions behind those methods are just too much to swallow. Embarrassingly, however, I can never retain the tricks that make it work, so I break this out as a notebook in large part to force myself to re-re-re-learn it, and make it stick this time.

See also:
• Large Deviations
• Statistics

To read:
• Gianfranco Adimari and Annamaria Guolo, "A note on the asymptotic behaviour of empirical likelihood statistics", Statistical Methods and Applications 19 (2010): 463--476
• Jianqing Fan and Jian Zhang, "Sieve empirical likelihood ratio tests for nonparametric functions", Annals of Statistics 32 (2004): 1858--1907, math.ST/0503667
• Nils Lid Hjort, Ian W. McKeague, Ingrid Van Keilegom, "Extending the scope of empirical likelihood", Annals of Statistics 37 (2009): 1079--1111, arxiv:0904.2949
• Yuichi Kitamura, "Empirical Likelihood Methods in Econometrics: Theory and Practice", ssrn/917901 (2006)
• K. L. Mengersen, P. Pudlo, C. P. Robert, "Bayesian computation via empirical likelihood", arxiv:1205.5658
• Art Owen, Empirical Likelihood
• Hanxiang Peng and Anton Schick, "Empirical likelihood approach to goodness of fit testing", Bernoulli 19 (2013): 954--981
• Susanne M. Schennach, "Point estimation with exponentially tilted empirical likelihood", Annals of Statistics 35 (2007): 634--672, arxiv:0708.1874