The Experts below are selected from a list of 6192 Experts worldwide ranked by ideXlab platform
Taesung Kim - One of the best experts on this subject based on the ideXlab platform.
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Almost Sure Convergence for Linear Processes Generated by Positively Dependent Processes
Stochastic Analysis and Applications, 2004Co-Authors: Taesung Kim, Dong Ho ParkAbstract:In this article we obtain strong laws of large numbers for linear processes generated by sequences of random variables, which are either linearly Positive Quadrant dependent or associated. No stationarity is required.
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on a central limit theorem for a stationary multivariate linear process generated by linearly Positive Quadrant dependent random vectors
Journal of Korean Medical Science, 2002Co-Authors: Taesung KimAbstract:For a stationary multivariate linear process of the form Xt = 1 X j=0 AjZtij, where fZt : t = 0;§1;§2;¢¢¢g is a sequence of stationary linearly Positive Quadrant dependent m-dimensional random vectors with E(Zt) = O and EkZtk 2 < 1, we prove a
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a central limit theorem for a stationary linear process generated by linearly Positive Quadrant dependent process
Communications for Statistical Applications and Methods, 2001Co-Authors: Taesung KimAbstract:A central limit theorem is obtained for stationary linear process of the form X_t=Σ^∞_(j=0)a_je_(t-j), where {e_t} is a strictly stationary sequence of linearly Positive Quadrant dependent random variables with Ee_t=0, Ee^2_t<∞ and {a_j} is a sequence of real numbers with Σ^∞_(j=0)|a_j|<∞ we also derive a functional central limit theorem for this linear process.
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on the strong law of large numbers for linearly Positive Quadrant dependent random variables
Communications of The Korean Mathematical Society, 1998Co-Authors: Taesung Kim, Hyeyoung SeoAbstract:In this note we derive inequalities of linearly Positive Quadrant dependent random variables and obtain a strong law of large numbers for linealy Positive quardant dependent random variables. Our results imply an extension of Birkel's strong law of large numbers for associated random variables to the linear Positive Quadrant dependence case.
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A FUNCTIONAL CENTRAL LIMIT THEOREM FOR LINEARLY Positive Quadrant DEPENDENT RANDOM FIELDS
1997Co-Authors: Taesung Kim, Eun-yang SeokAbstract:In this note we prove a functional central limit theorem for nonsta- tionary linearly Positive Quadrant dependent random fields. We extend it to the case of random measures.
Pierre Tarrès - One of the best experts on this subject based on the ideXlab platform.
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Inverting Ray-Knight identity
Probability Theory and Related Fields, 2015Co-Authors: Christophe Sabot, Pierre TarrèsAbstract:International audienceWe provide a short proof of the Ray-Knight second generalized Theorem, using a martingale which can be seen (on the Positive Quadrant) as the Radon–Nikodym derivative of the reversed vertex-reinforced jump process measure with respect to the Markov jump process with the same conductances. Next we show that a variant of this process provides an inversion of that Ray-Knight identity. We give a similar result for the Ray-Knight first generalized Theorem
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Ray-Knight Theorem: a short proof
2013Co-Authors: Christophe Sabot, Pierre TarrèsAbstract:We provide a short proof of the Ray-Knight second generalized Theorem, using a martingale which can be seen (on the Positive Quadrant) as the Radon-Nikodym derivate of the reversed vertex-reinforced jump process measure with respect to the Markov jump process with the same conductances.
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Inverting Ray-Knight identity
arXiv: Probability, 2013Co-Authors: Christophe Sabot, Pierre TarrèsAbstract:We provide a short proof of the Ray-Knight second generalized Theorem, using a martingale which can be seen (on the Positive Quadrant) as the Radon-Nikodym derivative of the reversed vertex-reinforced jump process measure with respect to the Markov jump process with the same conductances. Next we show that a variant of this process provides an inversion of that Ray-Knight identity. We give a similar result for the Ray-Knight first generalized Theorem.
Joshua M. Tebbs - One of the best experts on this subject based on the ideXlab platform.
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Testing for Positive Quadrant Dependence
The American Statistician, 2019Co-Authors: Chuan-fa Tang, Dewei Wang, Hammou El Barmi, Joshua M. TebbsAbstract:AbstractWe develop an empirical likelihood (EL) approach to test independence of two univariate random variables X and Y versus the alternative that X and Y are strictly Positive Quadrant dependent...
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Testing for Positive Quadrant Dependence
The American Statistician, 2019Co-Authors: Chuan-fa Tang, Dewei Wang, Hammou El Barmi, Joshua M. TebbsAbstract:We develop an empirical likelihood (EL) approach to test independence of two univariate random variables X and Y versus the alternative that X and Y are strictly Positive Quadrant dependent (PQD). ...
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Testing for Positive Quadrant dependence
2019Co-Authors: Chuan-fa Tang, Dewei Wang, Hammou El Barmi, Joshua M. TebbsAbstract:We develop an empirical likelihood approach to test independence of two univariate random variables X and Y versus the alternative that X and Y are strictly Positive Quadrant dependent (PQD). Establishing this type of ordering between X and Y is of interest in many applications, including finance, insurance, engineering, and other areas. Adopting the framework in Einmahl and McKeague (2003, Bernoulli), we create a distribution-free test statistic that integrates a localized empirical likelihood ratio test statistic with respect to the empirical joint distribution of X and Y. When compared to well known existing tests and distance-based tests we develop by using copula functions, simulation results show the EL testing procedure performs well in a variety of scenarios when X and Y are strictly PQD. We use three data sets for illustration and provide an online R resource practitioners can use to implement the methods in this article.
Christophe Sabot - One of the best experts on this subject based on the ideXlab platform.
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Inverting Ray-Knight identity
Probability Theory and Related Fields, 2015Co-Authors: Christophe Sabot, Pierre TarrèsAbstract:International audienceWe provide a short proof of the Ray-Knight second generalized Theorem, using a martingale which can be seen (on the Positive Quadrant) as the Radon–Nikodym derivative of the reversed vertex-reinforced jump process measure with respect to the Markov jump process with the same conductances. Next we show that a variant of this process provides an inversion of that Ray-Knight identity. We give a similar result for the Ray-Knight first generalized Theorem
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Ray-Knight Theorem: a short proof
2013Co-Authors: Christophe Sabot, Pierre TarrèsAbstract:We provide a short proof of the Ray-Knight second generalized Theorem, using a martingale which can be seen (on the Positive Quadrant) as the Radon-Nikodym derivate of the reversed vertex-reinforced jump process measure with respect to the Markov jump process with the same conductances.
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Inverting Ray-Knight identity
arXiv: Probability, 2013Co-Authors: Christophe Sabot, Pierre TarrèsAbstract:We provide a short proof of the Ray-Knight second generalized Theorem, using a martingale which can be seen (on the Positive Quadrant) as the Radon-Nikodym derivative of the reversed vertex-reinforced jump process measure with respect to the Markov jump process with the same conductances. Next we show that a variant of this process provides an inversion of that Ray-Knight identity. We give a similar result for the Ray-Knight first generalized Theorem.
Zbigniew Palmowski - One of the best experts on this subject based on the ideXlab platform.
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De Finetti's dividend problem and impulse control for a two-dimensional insurance risk process
Stochastic Models, 2011Co-Authors: Irmina Czarna, Zbigniew PalmowskiAbstract:Consider two insurance companies (or two branches of the same company) that receive premiums at different rates and then split the amount they pay in fixed proportions for each claim (for simplicity we assume that they are equal). We model the occurrence of claims according to a Poisson process. The ruin is achieved when the corresponding two-dimensional risk process first leaves the Positive Quadrant. We will consider two scenarios of the controlled process: refraction and impulse control. In the first case the dividends are payed out when the two-dimensional risk process exits the fixed region. In the second scenario, whenever the process hits the horizontal line, it is reduced by paying dividends to some fixed point in the Positive Quadrant where it waits for the next claim to arrive. In both models we calculate the discounted cumulative dividend payments until the ruin. This paper is the first attempt to understand the effect of dependencies of two portfolios on the joint optimal strategy of paying dividends. For example in case of proportional reinsurance one can observe the interesting phenomenon that choice of the optimal barrier depends on the initial reserves. This is in contrast with the one-dimensional Cram\'{e}r-Lundberg model where the optimal choice of the barrier is uniform for all initial reserves.
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a two dimensional ruin problem on the Positive Quadrant
arXiv: Probability, 2007Co-Authors: Florin Avram, Zbigniew Palmowski, Martijn PistoriusAbstract:In this paper we study the joint ruin problem for two insurance companies that divide between them both claims and premia in some specified proportions (modeling two branches of the same insurance company or an insurance and re-insurance company). Modeling the risk processes of the insurance companies by Cram\'{e}r-Lundberg processes we obtain the Laplace transform in space of the probability that either of the insurance companies is ruined in finite time. Subsequently, for exponentially distributed claims, we derive an explicit analytical expression for this joint ruin probability by explicitly inverting this Laplace transform. We also provide a characterization of the Laplace transform of the joint ruin time.