Models of unobserved heterogeneity, or frailty as it is commonly known in survival analysis, can often be formulated as semiparametric mixture models and estimated by maximum likelihood as proposed by Robbins (1950) and elaborated by Kiefer and Wolfowitz (1956). Recent developments in convex...

Statistical models of unobserved heterogeneity are typically formalized as mixtures of simple parametric models and interest naturally focuses on testing for homogeneity versus general mixture alternatives. Many tests of this type can be interpreted as C(α) tests, as in Neyman (1959), and shown...

These notes are an informal, first installment in an ongoing project to develop a convenient template for computational experimentation in econometrics. The approach is illustrated by means of an example based on some current research with Steve Portnoy on improving the speed of quantile...

We consider several variants of a likelihood-ratio process for quantile regression designed to test composite hypotheses about the combined influence of several covariates over an entire range of conditional quantile functions. A closely related process is proposed as a goodness-of-fit criterion...

A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics. In contrast to prior proposals for quantile regression estimation of...

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that...