Empirical Likelihood

By Art B. Owen, D. Sprott. 2001

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Citations in the corpus

Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning (2019, methodology): “Empirical likelihood is a nonparametric MLE commonly used in statistics [21].”
Variance-based Regularization with Convex Objectives (2016, methodology): “In this paper, we develop an approach based on Owen’s empirical likelihood [19] and ideas from distributionally robust optimization [4, 5, 10] that—whenever the loss ` is convex—provides a tractable convex formulation closely approximating the penalized risk (3).”
On Bartlett correction of empirical likelihood in the presence of nuisance parameters (2006, background): “The empirical likelihood has been shown in a wide range of situations as outlined in Owen (2001) to admit limiting chisquare distributions, which is a nonparametric version of the Wilks’ theorem in parametric likelihood.”
Empirical likelihood ratio test for the change-point problem (2007, methodology): “Recently, Guan (2004) has considered a semiparametric changepoint model using empirical likelihood method (Owen, 1990, 2001) which is applied to detect the change from a distribution to a weighted one.”
On empirical likelihood for semiparametric two-sample density ratio models (2008, methodology): “The EL estimate of θT , proposed by Qin (1998), is then θ̃ := arg sup θ∈Θ l(θ).”
Covariate balancing propensity score (2014, methodology): “We estimate the CBPS by using the moment conditions based on the covariate balancing property under the GMM or EL framework (see Hayashi (2000) and Owen (2001) for an accessible introduction of each method).”
Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations (2007, background): “For a more complete disclosure of recent references and development we refer to the book by Owen (2001).”
Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, methodology): “Note that this is the typical formulation of empirical likelihood method (Qin & Lawless, 1994; Owen, 2001).”
ADJUSTED EMPIRICAL LIKELIHOOD WITH HIGH-ORDER PRECISION (2010, background): “(1) The empirical log-likelihood ratio function is defined by Rn(θ) =−2 log(nn× Ln(θ)) [see Owen (2001) and Qin and Lawless (1994)].”
Jackknife Empirical Likelihood Test for Equality of Two High Dimensional Means (2013, unknown): “Under conditions of Theorem 1, we have as min{n1, n2} → ∞ 1 √ m1 m1 ∑ i=1 XT i X̄i √ ρ1 d → N(0, 1), (12) 1 √ m2 m2 ∑ j=1 Y T j Ȳj √ ρ2 d → N(0, 1), (13) 1 √ m1 m1 ∑ i=1 αT (Xi + X̄i) √ τ1 d → N(0, 1), (14) and 1 √ m2 m2 ∑ j=1 αT (Yj + Ȳj) √ τ2 d → N(0, 1).”
Empirical Likelihood Estimation Using Auxiliary Summary Information with Different Covariate Distributions (2019, methodology): “Applying the semiempirical likelihood method (Qin (2000); Chatterjee et al.”
Combining information from multiple surveys through the empirical likelihood method (2004, unknown): “See Owen (2001) for a comprehensive account (and updated overview) of the subject.”
Empirical likelihood on the full parameter space (2013, methodology): “See also Owen (2001).”
Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications (2010, unknown): “The distance used here is Kullback–Leibler divergence, although other distance measures can be used (Cressie and Read 1984) and has an important connection to the empirical likelihood approach of Owen (2001).”
Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators (2003, methodology): “Furthermore, even when the data are discrete, so that an Edgeworth approximation is not valid, these calculations can be used for higher order efficiency comparisons, as in Pfanzagl and Wefelmeyer (1978). We also note that in the Appendix we give a corresponding expansion for λ̂, which may be of int”