modeling dependence structure in financial economics is of paramount importance when estimating portfolio’s value at risk, since risk of an asset in addition to its own behavior is also dependent on the behavior of other assets in the portfolio. application of joint distribution copula is one of the methods for incorporation dependence at lower and upper tail of returns’ distribution in financi...

Semilinear stochastic evolution equations with multiplicative L'evy noise are considered‎. ‎The drift term is assumed to be monotone nonlinear and with linear growth‎. ‎Unlike other similar works‎, ‎we do not impose coercivity conditions on coefficients‎. ‎We establish the continuous dependence of the mild solution with respect to initial conditions and also on coefficients. ‎As corollaries of ...

Systemic weather risk is a major obstacle for the formation of private (nonsubsidized) crop insurance. This paper explores the possibility of spatial diversification of insurance by estimating the joint occurrence of unfavorable weather conditions in different locations. For that purpose copula methods are employed that allow an adequate description of stochastic dependencies between multivaria...

A stochastic tail order is introduced to compare right tails of distributions and related closure properties are established. The stochastic tail order is then used to compare the dependence structure of multivariate extreme value distributions in terms of upper tail behaviors of their underlying samples.

The first laboratory confirmation of stochastic growth theory is reported. Floating potential fluctuations are measured in a vacuum arc centrifuge using a Langmuir probe. Statistical analysis of the energy density reveals a lognormal distribution over roughly 2 orders of magnitude, with a high-field nonlinear cutoff whose spatial dependence is consistent with the predicted eigenmode profile. Th...

This paper introduces a nonparametric copula-based index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals. Our index, termed Copula Index for Detecting Dependence and Monotonicity (CIM ), satisfies several desirable properties of measures of association, including Rényi’s properties, the ...

Currently, gene finding and RNA secondary structure prediction are performed separately. This is only valid if, for any DNA sequence the location of genes and corresponding RNA secondary structure can assumed to be independent. I develop methods that take a hidden Markov model for gene finding and a stochastic context-free grammar for RNA secondary structure prediction and from these, construct...

Currently, gene finding and RNA secondary structure prediction are performed separately. This is only valid if, for any DNA sequence the location of genes and corresponding RNA secondary structure can assumed to be independent. I develop methods that take a hidden Markov model for gene finding and a stochastic context-free grammar for RNA secondary structure prediction and from these, construct...

In this paper we prove some stochastic comparisons results for progressive type II censored order statistics. The problem of stochastically comparing concomitants of the two progressive type II censored order statistics with possibly different schemes, under different kinds of dependence between X and Y is considered and it is proved that if Y is stochastically increasing (decreasing) in X, ...

For exponential open and closed queueing networks, we investigate the internal dependence structure, compare the internal dependence for different networks, and discuss the relation of correlation formulae to the existence of spectral gaps and comparison of asymptotic variances. A central prerequisite for the derived theorems is stochastic monotonicity of the networks. The dependence structure ...