In order to evaluate the importance of higher-order correlations in neural spike count codes, flexible statistical models of dependent multivariate spike counts are required. Copula families, parametric multivariate distributions that represent dependencies, can be applied to construct such models. We introduce the Frank mixture family as a new copula family that has separate parameters for all...

This article examines the ability of time-varying Gaussian and Student t copulas to accurately predict the probability of joint extreme co-movements in stock index returns. Using a sample of more than 20 years of daily return observations of the Eurostoxx 50 and Dow Jones Industrial 30 stock indices, Gaussian and Student t copulas are calibrated daily on a rolling window of the 250 most recent ...

Imipramine-induced ex copula ejaculates (11) and fractionated in copula ejaculates were collected from each of 5 pony stallions for freezing in 5-ml straws (6), using a modified Kenney glucose skim-milk extender (2). Initial post-thaw total and progressive motilities and daily post-thaw total and progressive motilities, as well as the number of days to reach 0 progressively motile spermatozoa, ...

Goodness-of-fit tests are a fundamental element in the copula-based modeling of multivariate continuous distributions. Among the different procedures proposed in the literature, recent large scale simulations suggest that one of the most powerful tests is based on the empirical process comparing the empirical copula with a parametric estimate of the copula derived under the null hypothesis. As ...

Recently Chen and Fan (2003a) introduced a new class of semiparametric copula-based multivariate dynamic (SCOMDY) models. A SCOMDY model specifies the conditional mean and the conditional variance of a multivariate time series parametrically (such as VAR, GARCH), but specifies the multivariate distribution of the standardized innovation semiparametrically as a parametric copula evaluated at non...

In this dissertation, control methodologies for systems of multiple mobile agents facing multiple objectives are considered. Carefully constructed objective functions for agents interested in any or all of collision avoidance, robust communications, waypoint following, and dynamic coverage objectives are presented. Theory relevant to gradient-like control laws designed using over-approximations...

We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatialtemporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is domin...

We describe a model that takes into account the tail dependence present in a large set of historical risk factor data using the modern concept of copulas. We extend the popular t-copula to obtain a new grouped t-copula which describes more accurately the dependence among risk factors of different classes. We explain how to estimate the parameters of the grouped t-copula and apply the method to ...

In this paper, we provide two one-factor heavy-tailed copula models for pricing a collateralized debt obligation and credit default index swap tranches: (1) a one-factor double t distribution with fractional degrees of freedom copula model and (2) a one-factor double mixture distribution of t and Gaussian distribution copula model. A tail-fatness parameter is introduced in each model, allowing ...

AMS 2000 subject class: Primary 62H20 60F05 62G20 Secondary 60G15 62G10 Keywords: Multivariate measure of association Copula Nonparametric estimation Empirical copula process Weak convergence Nonparametric bootstrap Strong mixing a b s t r a c t A multivariate measure of association is proposed, which extends the bivariate copula-based measure Phi-Square introduced by Hoeffding [22]. We discuss...