Abstract Some linkages between kernel and penalty methods of density estimation are explored. It is recalled that classical Gaussian kernel density estimation can be viewed as the solution of the heat equation with initial condition given by data. We then observe that there is a direct...

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This paper explores the robustness of minimum distance (GMM) estimators focusing particularly on the effect of intermediate covariance matrix estimation on final estimator performance. Asymptotic expansions to order <italic>O</italic>(<italic>n</italic>^−3/2) are employed to construct <italic>O</italic>(<italic>n</italic>⁻²) expansions for the variance of...

When we control for worker characteristics, we are able to directly test theories of efficiency wages and fairness. We find that high-quality workers continue to be associated with good outcomes. High-wage strategies are also associated with better won-lost performance and higher attendance...

Given a scalar random variable Y and a random vector X defined on the same probability space, the conditional distribution of Y given X can be represented by either the conditional distribution function or the conditional quantile function. To these equivalent representations correspond two...

Estimation of mixture densities for the classical Gaussian compound decision problem and their associated (empirical) Bayes rules is considered from two new perspectives. The first, motivated by Brown and Greenshtein, introduces a nonparametric maximum likelihood estimator of the mixture density...