The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods, due to large numbers of irrelevant covariates in practice. In this paper, we study...

Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

We consider theoretical bootstrap "coupling" techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of "coupling" bootstrap techniques are developed for additive models with both symmetric error...

The fuzzy transform (F-transform), introduced by I. Perfilieva, is a powerful tool for the construction of fuzzy approximation models; it is based on generalized fuzzy partitions and it is obtained by minimizing a quadratic (L₂-norm) functional. In this paper we describe an analogous...

In this paper, we introduce a methodology based on zero-inflated long-term survival data in order to deal with fraud rate estimation in bank loan portfolios. Our approach enables us to accommodate three different types of loan borrowers, i.e., fraudsters, those who are susceptible to default...

It is common practice to forecast social, political, and economic outcomes by polling people about their intentions. This approach is direct, but it can be unreliable in settings where it is hard to identify a representative sample, or where subjects have an incentive to conceal their true...

Generalized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for weakly dependent data and we prove oracle efficiency....

Experimental researchers routinely use regression in order to control for pre-treatment covariates. This practice has become controversial in the wake of recent demonstrations showing that this type of regression is prone to bias in small samples. Bias may even remain when units are sampled from...

This article is the first of a series discussing the Bayesian Network representation of multivariate normal distributions. A joint normal distribution being fully specified by its mean vector and its covariance matrix is not simple to interact with as its Bayesian network equivalent....