Large-sample estimation and inference in multivariate single-index models.

By optimizing index functions against different outcomes, we propose a multivariate single-index model (SIM) for development of medical indices that simultaneously work with multiple outcomes. Fitting of a multivariate SIM is not fundamentally different from fitting a univariate SIM, as the former can be written as a sum of multiple univariate SIMs with appropriate indicator functions. What have not been carefully studied are the theoretical properties of the parameter estimators.

A unified approach for analyzing exchangeable binary data with applications to developmental toxicity studies.

In this article, we present a general procedure to analyze exchangeable binary data that may also be viewed as realizations of binomial mixtures. Our approach unifies existing models and is practical and computationally easy. Resulting from completely monotonic functions, we introduce a rich family of parametric parsimonious binomial mixtures, including the incomplete Beta-, Gamma-, Normal-, and Poisson-binomial, generalizing the Beta-binomial.

Outlier detection with the kernelized spatial depth function.

Statistical depth functions provide from the "deepest" point a "center-outward ordering" of multidimensional data. In this sense, depth functions can measure the "extremeness" or "outlyingness" of a data point with respect to a given data set. Hence, they can detect outliers--observations that appear extreme relative to the rest of the observations.

Robust clustering in high dimensional data using statistical depths.

Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor center representative for high dimensional data. We need a new algorithm that is robust and works well in high dimensional data sets e.