The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Barbara Vantaggi - One of the best experts on this subject based on the ideXlab platform.
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independence and conditional possibility for Strictly Monotone triangular norms research articles
International Journal of Intelligent Systems, 2006Co-Authors: Laura Ferracuti, Barbara VantaggiAbstract:In the literature there are different definitions of conditional possibility. Starting from a general axiomatic definition, we propose a definition of independence for o-conditional possibility, in the case that o is a Strictly Monotone triangular norm. We study its main properties to compare it to other definitions introduced in possibility theory. Then, we show that the controversial aspects related to logical dependencies (structural zeros) can be circumvented. Moreover, a set of properties (the well-known graphoid properties) has been considered to be tested, allowing us to compare the proposed definition to the independence notions given in the context of other uncertainty formalisms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 299–323, 2006.
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independence and conditional possibility for Strictly Monotone triangular norms
International Journal of Intelligent Systems, 2006Co-Authors: Laura Ferracuti, Barbara VantaggiAbstract:In the literature there are different definitions of conditional possibility. Starting from a general axiomatic definition, we propose a definition of independence for ⊙-conditional possibility, in the case that 0 is a Strictly Monotone triangular norm. We study its main properties to compare it to other definitions introduced in possibility theory. Then, we show that the controversial aspects related to logical dependencies (structural zeros) can be circumvented. Moreover, a set of properties (the well-known graphoid properties) has been considered to be tested, allowing us to compare the proposed definition to the independence notions given in the context of other uncertainty formalisms.
W U Zhengpeng - One of the best experts on this subject based on the ideXlab platform.
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study on the strengthening buffer operator based on the Strictly Monotone function
Journal of Communication University of China, 2013Co-Authors: W U ZhengpengAbstract:Based on the present theories of buffer operators,We propose in this paper several kinds of buffer operators based on the Strictly Monotone function,which all have the universality and practicability.we prove them to be strengthening buffer operators.The problem of some contradictions between qualitative analysis and quantiative forecast in pretreatment for vibration data sequences is resolved effectively.
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analysis on the strengthening buffer operator based on the Strictly Monotone function
International Journal of Applied Physics and Mathematics, 2013Co-Authors: Hu Xiaoli, W U ZhengpengAbstract:We construct four kinds of new strengthening buffer operators by using inverse function theorem based on the axiom system of buffer operator. And demonstrate the GuanShi strengthening buffer operator which we compare with is a special case of our new operators. After studying the inner link and characteristics between the GuanShi and our new buffer operators, we greatly develop the application scope of strengthening buffer operator. This paper researches on buffer operators' construction with functions and gives a new direction for construction of buffer operators.
Laura Ferracuti - One of the best experts on this subject based on the ideXlab platform.
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independence and conditional possibility for Strictly Monotone triangular norms research articles
International Journal of Intelligent Systems, 2006Co-Authors: Laura Ferracuti, Barbara VantaggiAbstract:In the literature there are different definitions of conditional possibility. Starting from a general axiomatic definition, we propose a definition of independence for o-conditional possibility, in the case that o is a Strictly Monotone triangular norm. We study its main properties to compare it to other definitions introduced in possibility theory. Then, we show that the controversial aspects related to logical dependencies (structural zeros) can be circumvented. Moreover, a set of properties (the well-known graphoid properties) has been considered to be tested, allowing us to compare the proposed definition to the independence notions given in the context of other uncertainty formalisms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 299–323, 2006.
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independence and conditional possibility for Strictly Monotone triangular norms
International Journal of Intelligent Systems, 2006Co-Authors: Laura Ferracuti, Barbara VantaggiAbstract:In the literature there are different definitions of conditional possibility. Starting from a general axiomatic definition, we propose a definition of independence for ⊙-conditional possibility, in the case that 0 is a Strictly Monotone triangular norm. We study its main properties to compare it to other definitions introduced in possibility theory. Then, we show that the controversial aspects related to logical dependencies (structural zeros) can be circumvented. Moreover, a set of properties (the well-known graphoid properties) has been considered to be tested, allowing us to compare the proposed definition to the independence notions given in the context of other uncertainty formalisms.
Holger Dette - One of the best experts on this subject based on the ideXlab platform.
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Strictly Monotone and smooth nonparametric regression for two or more variables
Canadian Journal of Statistics-revue Canadienne De Statistique, 2006Co-Authors: Holger Dette, Regine SchederAbstract:The authors propose a new Monotone nonparametric estimate for a regression function of two or more variables. Their method consists in applying successively one-dimensional isotonization procedures on an initial, unconstrained nonparametric regression estimate. In the case of a Strictly Monotone regression function, they show that the new estimate and the initial one are first-order asymptotic equivalent; they also establish asymptotic normality of an appropriate standardization of the new estimate. In addition, they show that if the regression function is not Monotone in one of its arguments, the new estimate and the initial one have approximately the same Lp-norm. They illustrate their approach by means of a simulation study, and two data examples are analyzed. Regression non parametrique lisse et strictement Monotone de deux variables ou plus Les auteurs proposent un nouvel estimateur non parametrique Monotone de la fonction de regression de deux variables ou plus. Leur methode consiste a appliquer une serie de procedures d'isotonisation univariees a un estimateur non parametrique de la regression qui est non-contraint au depart. Lorsque la regression est strictement Monotone, ils montrent que les estimateurs initial et transforme sont asymp-totiquement equivalents au premier ordre; ils etablissent aussi la normalite asymptotique d'une version proprement staindardisee du nouvel estimateur. Ils montrent de plus que dans les cas ou la regression n'est pas Monotone en l'un de ses arguments, les deux estimateurs ont a peu pres la měme norme Lp. Ils illustrent leur approche au moyen de simulations et sur deux jeux de donnees.
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a simple nonparametric estimator of a Strictly Monotone regression function
Bernoulli, 2006Co-Authors: Holger Dette, Natalie Neumeyer, Kay F PilzAbstract:A new method for Monotone estimation of a regression function is proposed, which is potentially attractive to users of conventional smoothing methods. The main idea of the new approach is to construct a density estimate from the estimated values m(i/N) (i = 1, ..., N) of the regression function and to use these 'data' for the calculation of an estimate of the inverse of the regression function. The final estimate is then obtained by a numerical inversion. Compared to the currently available techniques for Monotone estimation the new method does not require constrained optimization. We prove asymptotic normality of the new estimate and compare the asymptotic properties with the unconstrained estimate. In particular, it is shown that for kernel estimates or local polynomials the bandwidths in the procedure can be chosen such that the Monotone estimate is first order asymptotically equivalent to the unconstrained estimate. We also illustrate the performance of the new procedure by means of a simulation study.
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Strictly Monotone and smooth nonparametric regression for two or more variables
Technical reports, 2005Co-Authors: Regine Scheder, Holger DetteAbstract:In this article a new Monotone nonparametric estimate for a regression function of two or more variables is proposed. The method starts with an unconstrained nonparametric regression estimate and uses successively one-dimensional isotonization procedures. In the case of a Strictly Monotone regression function, it is shown that the new estimate is first order asymptotic equivalent to the unconstrained estimate, and asymptotic normality of an appropriate standardization of the estimate is established. Moreover, if the regression function is not Monotone in one of its arguments, the constructed estimate has approximately the same Lp-norm as the initial unconstrained estimate. The methodology is also illustrated by means of a simulation study, and two data examples are analyzed.
Regine Scheder - One of the best experts on this subject based on the ideXlab platform.
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Strictly Monotone and smooth nonparametric regression for two or more variables
Canadian Journal of Statistics-revue Canadienne De Statistique, 2006Co-Authors: Holger Dette, Regine SchederAbstract:The authors propose a new Monotone nonparametric estimate for a regression function of two or more variables. Their method consists in applying successively one-dimensional isotonization procedures on an initial, unconstrained nonparametric regression estimate. In the case of a Strictly Monotone regression function, they show that the new estimate and the initial one are first-order asymptotic equivalent; they also establish asymptotic normality of an appropriate standardization of the new estimate. In addition, they show that if the regression function is not Monotone in one of its arguments, the new estimate and the initial one have approximately the same Lp-norm. They illustrate their approach by means of a simulation study, and two data examples are analyzed. Regression non parametrique lisse et strictement Monotone de deux variables ou plus Les auteurs proposent un nouvel estimateur non parametrique Monotone de la fonction de regression de deux variables ou plus. Leur methode consiste a appliquer une serie de procedures d'isotonisation univariees a un estimateur non parametrique de la regression qui est non-contraint au depart. Lorsque la regression est strictement Monotone, ils montrent que les estimateurs initial et transforme sont asymp-totiquement equivalents au premier ordre; ils etablissent aussi la normalite asymptotique d'une version proprement staindardisee du nouvel estimateur. Ils montrent de plus que dans les cas ou la regression n'est pas Monotone en l'un de ses arguments, les deux estimateurs ont a peu pres la měme norme Lp. Ils illustrent leur approche au moyen de simulations et sur deux jeux de donnees.
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Strictly Monotone and smooth nonparametric regression for two or more variables
Technical reports, 2005Co-Authors: Regine Scheder, Holger DetteAbstract:In this article a new Monotone nonparametric estimate for a regression function of two or more variables is proposed. The method starts with an unconstrained nonparametric regression estimate and uses successively one-dimensional isotonization procedures. In the case of a Strictly Monotone regression function, it is shown that the new estimate is first order asymptotic equivalent to the unconstrained estimate, and asymptotic normality of an appropriate standardization of the estimate is established. Moreover, if the regression function is not Monotone in one of its arguments, the constructed estimate has approximately the same Lp-norm as the initial unconstrained estimate. The methodology is also illustrated by means of a simulation study, and two data examples are analyzed.
