With count-valued outcomes y in {0,1,...,M} identification and estimation of average treatment effects raise no special considerations beyond those involved in the continuous-outcome case. If partial identification of the distribution of treatment effects is of interest, however, count-valued...

Estimating covariance matrices is an important part of portfolio selection, risk management, and asset pricing. This paper reviews the recent development in estimating high dimensional covariance matrices, where the number of variables can be greater than the number of observations. The...

In small samples and especially in the case of small true default probabilities, standard approaches to credit default probability estimation have certain drawbacks. Most importantly, standard estimators tend to underestimate the true default probability which is of course an undesirable...

Estimation of threshold parameters in (generalized) threshold regression models is typically performed by maximizing the corresponding profile likelihood function. Also, certain Bayesian techniques based on non-informative priors are developed and widely used. This article draws attention to...

A method of small-area estimation with a utility function is developed. The utility characterises a policy planned to be implemented in each area, based on the area’s estimate of a key quantity. It is shown that the commonly applied empirical Bayes and composite estimators are inefficient for...

Estimating covariance matrices is an important part of portfolio selection, risk management, and asset pricing. This paper reviews the recent development in estimating high dimensional covariance matrices, where the number of variables can be greater than the number of observations. The...

This article explores the statistical methodologies used in demonstration and effectiveness studies when the treatments are applied across multiple settings. The importance of evaluating and how to evaluate these types of studies are discussed. As an alternative to standard methodology, the...

The empirical Bayes (EB) estimator or empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation in the sense of increasing the precision of estimation of small area means. However, one potential difficulty of EB is that when...