Approximate Bayesian Computation (ABC) provides Monte Carlo inference of the posterior distribution, even for models with intractable likelihoods. The quality of ABC inference relies on the choice of tolerance for the distance between the observed data summary statistics, and the summary...

The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric regression settings. Drawing on the regression literature, the...

The asymmetry in the tail dependence between U.S. equity portfolios and the aggregate U.S. market is a well-established property. Given the limited number of observations in the tails of a joint distribution, standard non-parametric measures of tail dependence have poor finite-sample properties...

Acyclic probabilistic finite automata (APFA) constitute a rich family of models for discrete longitudinal data. An APFA may be represented as a directed multigraph, and embodies a set of context-specific conditional independence relations that may be read off the graph. A model selection...

A new lack-of-fit test for quantile regression models, that is suitable even with high-dimensional covariates, is proposed. The test is based on the cumulative sum of residuals with respect to unidimensional linear projections of the covariates. To approximate the critical values of the test, a...

Computational intensity and sequential nature of estimation techniques for Bayesian methods in statistics and machine learning, combined with their increasing applications for big data analytics, necessitate both the identification of potential opportunities to parallelize techniques such as...

Local linear estimators for the conditional residual entropy function in the case of complete and censored samples are proposed. The resulting estimators are shown to be consistent and asymptotically normally distributed under certain regularity conditions. The performance of the estimator is...

When the exact unbiasedness condition is relaxed to a near unbiasedness condition, this short communication shows that the best linear near unbiased estimation problem is actually a semi-infinite programming problem. Our recently developed dual parameterization method is applied for solving the...

The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and...