Empirical Likelihood for Non‐Smooth Criterion Functions

By ELISA M. MOLANES LOPEZ, I. Van Keilegom, Noël Veraverbeke. 2009

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Bayesian Mode Regression (2012, methodology): “As the criterion function g ( X, Y, β ) results in non-smooth estimating equations we employ a similar method to the one use by Molanes Lopez et al. (2009), among others, to derive our asymptotic results.”
Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation (2017, methodology): “…some historical perspectives to this work, in its present form EL was introduced by Owen (1988) who followed by Qin and Lawless (1994) and Molanes Lopez et al. (2009) made significant contributions to the methodological study of the EL, particularly in developing novel statistical computations…”
A review on empirical likelihood methods for regression (2009, methodology): “Such a rate for λ is obtained in the original papers of Owen (1988, 1990) for the mean parameter (which can be treated as a trivial case of regression without covariates), in Owen (1991) for linear regression, and in Qin and Lawless (1994) and Molanes-Lopez et al. (2009) for the more general case ”