The Experts below are selected from a list of 24384 Experts worldwide ranked by ideXlab platform
Yong Zhu - One of the best experts on this subject based on the ideXlab platform.
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Reduction of fanning influence in two‐wave Mixing Coefficient measurements in thick crystals
Applied Physics Letters, 1996Co-Authors: Jiasen Zhang, Hong Gao, Yong ZhuAbstract:We propose and demonstrate a new method to measure the two‐wave Mixing Coefficient with slight influence of beam fanning in thick crystals with high coupling constants. The strong beam fanning was suppressed by introducing an additional erase beam. By measuring the effective two‐wave Mixing Coefficient with the additional erase beam, the coupling Coefficient without the additional erase beam can be calculated.
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reduction of fanning influence in two wave Mixing Coefficient measurements in thick crystals
Applied Physics Letters, 1996Co-Authors: Jiasen Zhang, Hong Gao, Yong ZhuAbstract:We propose and demonstrate a new method to measure the two‐wave Mixing Coefficient with slight influence of beam fanning in thick crystals with high coupling constants. The strong beam fanning was suppressed by introducing an additional erase beam. By measuring the effective two‐wave Mixing Coefficient with the additional erase beam, the coupling Coefficient without the additional erase beam can be calculated.
Pierre Ribereau - One of the best experts on this subject based on the ideXlab platform.
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Fitting spatial max-mixture processes with unknown extremal dependence class: an exploratory analysis tool
Test, 2019Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Pierre RibereauAbstract:One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultaneous extreme events. Following recent advances in the literature, a flexible model called max-mixture model has been introduced for modeling situations where the extremal dependence structure may vary with the distance. In this paper we propose a nonparametric model-free selection criterion for the Mixing Coefficient Our criterion is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The procedure is based on a nonlinear least squares between the theoretical madogram and the empirical one. We perform a simulation study and apply our criterion to daily precipitation over the East of Australia.
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A Model-Free Selection Criterion For The Mixing Coefficient Of Spatial Max-Mixture Models
arXiv: Statistics Theory, 2018Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Pierre RibereauAbstract:One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultaneous extreme events. Following recent advances in the literature, a flexible model called max-mixture model has been introduced for modeling situations where the extremal dependence structure may vary with the distance. In this paper we propose a nonparametric model-free selection criterion for the Mixing Coefficient Our criterion is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The procedure is based on a nonlinear least squares between the theoretical madogram and the empirical one. We perform a simulation study and apply our criterion to daily precipitation over the East of Australia.
Abdul-fattah Abu-awwad - One of the best experts on this subject based on the ideXlab platform.
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Fitting spatial max-mixture processes with unknown extremal dependence class: an exploratory analysis tool
Test, 2019Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Pierre RibereauAbstract:One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultaneous extreme events. Following recent advances in the literature, a flexible model called max-mixture model has been introduced for modeling situations where the extremal dependence structure may vary with the distance. In this paper we propose a nonparametric model-free selection criterion for the Mixing Coefficient Our criterion is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The procedure is based on a nonlinear least squares between the theoretical madogram and the empirical one. We perform a simulation study and apply our criterion to daily precipitation over the East of Australia.
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A Model-Free Selection Criterion For The Mixing Coefficient Of Spatial Max-Mixture Models
arXiv: Statistics Theory, 2018Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Pierre RibereauAbstract:One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultaneous extreme events. Following recent advances in the literature, a flexible model called max-mixture model has been introduced for modeling situations where the extremal dependence structure may vary with the distance. In this paper we propose a nonparametric model-free selection criterion for the Mixing Coefficient Our criterion is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The procedure is based on a nonlinear least squares between the theoretical madogram and the empirical one. We perform a simulation study and apply our criterion to daily precipitation over the East of Australia.
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Censored pairwise likelihood-based tests for Mixing Coefficient of spatial max-mixture models
arXiv: Statistics Theory, 2017Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Ribereau PierreAbstract:Max-mixture processes are defined as Z = max(aX, (1 -- a)Y) with X an asymptotic dependent (AD) process, Y an asymptotic independent (AI) process and a $\in$ [0, 1]. So that, the Mixing Coefficient a may reveal the strength of the AD part present in the max-mixture process. In this paper we focus on two tests based on censored pairwise likelihood estimates. We compare their performance through an extensive simulation study. Monte Carlo simulation plays a fundamental tool for asymptotic variance calculations. We apply our tests to daily precipitations from the East of Australia. Drawbacks and possible developments are discussed.
Jiasen Zhang - One of the best experts on this subject based on the ideXlab platform.
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Reduction of fanning influence in two‐wave Mixing Coefficient measurements in thick crystals
Applied Physics Letters, 1996Co-Authors: Jiasen Zhang, Hong Gao, Yong ZhuAbstract:We propose and demonstrate a new method to measure the two‐wave Mixing Coefficient with slight influence of beam fanning in thick crystals with high coupling constants. The strong beam fanning was suppressed by introducing an additional erase beam. By measuring the effective two‐wave Mixing Coefficient with the additional erase beam, the coupling Coefficient without the additional erase beam can be calculated.
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reduction of fanning influence in two wave Mixing Coefficient measurements in thick crystals
Applied Physics Letters, 1996Co-Authors: Jiasen Zhang, Hong Gao, Yong ZhuAbstract:We propose and demonstrate a new method to measure the two‐wave Mixing Coefficient with slight influence of beam fanning in thick crystals with high coupling constants. The strong beam fanning was suppressed by introducing an additional erase beam. By measuring the effective two‐wave Mixing Coefficient with the additional erase beam, the coupling Coefficient without the additional erase beam can be calculated.
Véronique Maume-deschamps - One of the best experts on this subject based on the ideXlab platform.
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Fitting spatial max-mixture processes with unknown extremal dependence class: an exploratory analysis tool
Test, 2019Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Pierre RibereauAbstract:One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultaneous extreme events. Following recent advances in the literature, a flexible model called max-mixture model has been introduced for modeling situations where the extremal dependence structure may vary with the distance. In this paper we propose a nonparametric model-free selection criterion for the Mixing Coefficient Our criterion is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The procedure is based on a nonlinear least squares between the theoretical madogram and the empirical one. We perform a simulation study and apply our criterion to daily precipitation over the East of Australia.
-
A Model-Free Selection Criterion For The Mixing Coefficient Of Spatial Max-Mixture Models
arXiv: Statistics Theory, 2018Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Pierre RibereauAbstract:One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultaneous extreme events. Following recent advances in the literature, a flexible model called max-mixture model has been introduced for modeling situations where the extremal dependence structure may vary with the distance. In this paper we propose a nonparametric model-free selection criterion for the Mixing Coefficient Our criterion is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The procedure is based on a nonlinear least squares between the theoretical madogram and the empirical one. We perform a simulation study and apply our criterion to daily precipitation over the East of Australia.
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Censored pairwise likelihood-based tests for Mixing Coefficient of spatial max-mixture models
arXiv: Statistics Theory, 2017Co-Authors: Abdul-fattah Abu-awwad, Véronique Maume-deschamps, Ribereau PierreAbstract:Max-mixture processes are defined as Z = max(aX, (1 -- a)Y) with X an asymptotic dependent (AD) process, Y an asymptotic independent (AI) process and a $\in$ [0, 1]. So that, the Mixing Coefficient a may reveal the strength of the AD part present in the max-mixture process. In this paper we focus on two tests based on censored pairwise likelihood estimates. We compare their performance through an extensive simulation study. Monte Carlo simulation plays a fundamental tool for asymptotic variance calculations. We apply our tests to daily precipitations from the East of Australia. Drawbacks and possible developments are discussed.