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We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is...
Using the classical estimation method of moments, we propose a new semiparametric estima- tion procedure for multi-parameter copula models. Consistency and asymptotic normality of the obtained estim...
Recent work has discussed the importance of multiplicative closure for the Markov mod- els used in phylogenetics. For continuous-time Markov chains, a sufficient condition for multiplicative closure...
Latent variable models represent a useful tool in the social sciences where the analyzed constructs cannot be directly observed and, hence, they are not measurable. However, a set of indicators rela...
The association between two random variables is often of primary interest in statistical research. In this paper semiparametric models for the association between random vectors X and Y are consider...
In this paper we study how perturbing a matrix changes its non-negative rank. We show that the non-negative rank is upper-semicontinuos and we describe some special families of perturbations. We apply...
In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version of this response, ...
We consider heteroscedastic nonparametric regression models, when both the mean function and variance function are unknown and to be estimated with nonparametric approaches. We derive convergence rate...
We derive a unified stochastic picture for the duality of a resampling-selection model with a branching-coalescing particle process (cf. MR2123250) and for the self-duality of Feller's branching diffu...
There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especially in connection with...

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