The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Mohamed Belalia - One of the best experts on this subject based on the ideXlab platform.
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On the asymptotic properties of the Bernstein estimator of the Multivariate Distribution function
Statistics & Probability Letters, 2016Co-Authors: Mohamed BelaliaAbstract:The purpose of this paper is to study the asymptotic properties of Bernstein estimator of the Multivariate Distribution function, such as asymptotic bias, variance and asymptotic normality. Besides, we give the optimal choice of the polynomial order in terms of MSEs.
Alex J Cannon - One of the best experts on this subject based on the ideXlab platform.
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Multivariate quantile mapping bias correction an n dimensional probability density function transform for climate model simulations of multiple variables
Climate Dynamics, 2018Co-Authors: Alex J CannonAbstract:Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are Multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a Multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a Multivariate generalization of quantile mapping that transfers all aspects of an observed continuous Multivariate Distribution to the corresponding Multivariate Distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of Multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two Multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI Distribution and spatiotemporal autocorrelation of precipitation fields.
Anna Bartunova - One of the best experts on this subject based on the ideXlab platform.
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nonparametric estimation of Multivariate Distribution function for truncated and censored lifetime data
European Actuarial Journal, 2019Co-Authors: Valery Baskakov, Anna BartunovaAbstract:A number of models for generating statistical data in various fields of insurance, including life insurance, pensions, and general insurance have been considered. It is shown that the insurance statistics data, as a rule, are truncated and censored, and often Multivariate. We propose a non-parametric estimation of the Distribution function for Multivariate truncated-censored data in the form of a quasi-empirical Distribution and a simple iterative algorithm for its construction. To check the accuracy of the proposed evaluation of the Distribution function for truncated-censored data, simulation studies have been conducted, which showed its high efficiency. The proposed estimates have been tested for many years by the IAAC Group of Companies in the actuarial valuation of corporate social liabilities according to IAS 19 Employee Benefits. Apart from insurance, some results of the work can be used, for example in medicine, biology, demography, mathematical theory of reliability, etc.
Chiraz Labidi - One of the best experts on this subject based on the ideXlab platform.
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Return Interval, Dependence Structure and Multivariate Normality
Journal of Economics and Finance, 2004Co-Authors: Chiraz LabidiAbstract:We focus on changes in the Multivariate Distribution of index returns stemming purely from varying the return interval, assuming daily to quarterly returns. Whereas long-tailedness is present in daily returns, we find that, in agreement with a well-established idea, univariate return Distributions converge to normality as the return interval is lengthened. Such convergence does not occur, however, for Multivariate Distributions. Using a new method to parametrically model the dependence structure of stock index returns, we show that the persistence of a dependence structure implying negative asymptotic dependence in return series is the reason for the rejection of Multivariate normality for low return frequencies. Copyright Academy of Economics and Finance 2004
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Return Interval, Dependence Structure and Multivariate Normality
2001Co-Authors: Thierry Ané, Chiraz LabidiAbstract:We focus on changes in the Multivariate Distribution of index returns stemming purely from varying the return interval, assuming daily to quarterly returns. Whereas longtailedness is present in daily returns, we find that, in agreement with a well-established idea, univariate return Distributions converge to normality as the return interval is lengthened. Such convergence does not occur, however, for Multivariate Distributions. Using a new method to parametrically model the dependence structure implying negative asymptotic dependence in return series is the reason for the rejection of Multivariate normality for low return frequencies.
Manuel Úbeda-flores - One of the best experts on this subject based on the ideXlab platform.
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Best-Possible Bounds on Sets of Multivariate Distribution Functions
Communications in Statistics - Theory and Methods, 2005Co-Authors: José Antonio Rodríguez-lallena, Manuel Úbeda-floresAbstract:Abstract If H denotes the joint Distribution function of n random variables X 1, X 2,…, X n whose margins are F 1, F 2,…, F n , respectively, then the fundamental best-possible bounds inequality for H is F 2(x 2),…, F n (x n )) for all x 1, x 2,…, x n in [−∞, ∞]. In this paper we employ n-copulas and n-quasi-copulas to find similar bounds on arbitrary sets of Multivariate Distribution functions with given margins. We discuss bounds for an n-quasi-copula Q when a value of Q at a single point is known. As an application, we investigate about bounds for a Multivariate Distribution function H with given univariate margins when the value of H is known at a single point whose coordinates are percentiles of the variables X 1, X 2,…, X n , respectively.
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Distribution functions of Multivariate copulas
Statistics & Probability Letters, 2003Co-Authors: José Antonio Rodríguez-lallena, Manuel Úbeda-floresAbstract:For continuous random vectors X=(X1,X2,...,Xn) and Multivariate Distribution functions H1 and H2 with common univariate marginals, we study the Distribution function of the random variable H1(X) given that the joint Distribution function of X is H2. We show that the Distribution function of H1(X) depends only on the copulas C1 and C2 associated with H1 and H2, and examine various properties of these Distribution functions. We also illustrate some applications including Multivariate dependence orderings.
