Professor Roger Koenker

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.

The classical problem of the monopolist faced with an unknown demand curve is considered in a simple stochastic setting.

Efron's elegant approach to g-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.

We propose a new approach to computation of NPMLEs for binary response models that signicantly increase their computational tractability thereby facilitating greater exibility in applications.

Shape constraints play an increasingly prominent role in nonparametric function estimation. While considerable recent attention has been focused on log concavity as a regularizing device in nonparametric density estimation, weaker forms of concavity constraints encompassing larger classes of densities have received less attention but offer some additional flexibility.

Quantile regression 40 years on

Rebayes: an R package for empirical bayes mixture methods

Bayesian deconvolution: an R vinaigrette

Testing for homogeneity in mixture models

Empirical Bayes methods for Gaussian compound decision problems involving longitudinal data are considered.

Using PSID data we estimate a simple dynamic model of earnings that incorporates bivariate heterogeneity in intercept and variance of the innovation process. Prole likelihood is employed to estimate an AR(1) parameter controlling the persistence of the innovations.

We compare performance of the two approaches identifying circumstances in which each is preferred.

Data is reanalyzed from an important series of 19th century experiments conducted by C. S. Peirce and designed to study the plausibility of the Gaussian law of errors for astronomical observations.

Estimation of local, quantile-specific copula-based time series models offers some salient advantages over classical global parametric approaches.

Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated.

It is shown that a general form of pessimistic portfolio optimization based on the Choquet approach may be formulated as a problem of linear quantile regression.

Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes.