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Mohammad Hossein Aalamatsaz, Foroogh Mahpishanian,
Volume 5, Issue 1 (9-2011)
Abstract
There is a family of generalized Farlie-Gumbel-Morgenstern copulas, known as the semiparametric family, which is generated by a function called distribution-based generator. These generators have been studied typically for symmetric distributions in the literature. In this article, is proposed a method for asymmetric case which increases the flexibility of distribution-based generators and, thus, the model. In addition, a method for generalizing general generators is provided which can also be used to obtain more flexible distribution-based generators. Clearly, with more flexible generators more desirable models can be found to fit real data.
Samane Khosravi, Mohammad Amini, Gholamreza Mohtashami Borzadaran,
Volume 6, Issue 1 (8-2012)
Abstract
This paper explores the optimal criterion for comparison of some Phi-divergence measures. The dependence for generalized Farlie Gumbel Morgenstern family of copulas is numerically calculated and it has been shown that the Hellinger measure is the optimal criterion for measuring the divergence from independence.
Shahram Mansoury,
Volume 9, Issue 1 (9-2015)
Abstract
Jaynes' principle of maximum entropy states that among all the probability distributions satisfying some constraints, one should be selected which has maximum uncertainty. In this paper, we consider the methods of obtaining maximum entropy bivariate density functions via Taneja and Burg's measure of entropy under the constraints that the marginal distributions and correlation coefficient are prescribed. Next, a numerical method is considered. Finally, each method is illustrated via a numerical example.
Shahram Mansouri,
Volume 10, Issue 2 (2-2017)
Abstract
Among all statistical distributions, standard normal distribution has been the most important and practical distribution in which calculation of area under probability density function and cumulative distribution function are required. Unfortunately, the cumulative distribution function of this is, in general, expressed as a definite integral with no closed form or analytical solution. Consequently, it has to be approximated. In this paper, attempts have been made for Winitzki's approximation to be proved by a new approach. Then, the approximation is improved with some modifications and shown that the maximum error resulted from this is less than 0.0000584. Finally, an inverse function for computation of normal distribution quantiles has been derived.
Abouzar Bazyari,
Volume 11, Issue 1 (9-2017)
Abstract
The collective risk model of insurance company with constant initial capital when process of claims number have the poisson distribution with constant rate is considered. For computing the infinite time ruin probability the stochastic processes and differential equations are used. Also a formula is obtained to compute the Lundberg approximation in finding the approximate of infinite time ruin probability based on the distribution function of claims number. The numerical examples to illustrate these results are given and showed that for any value of initial capital the approximate of our infinite time ruin probability is closer to its real value rather than the ruin probability computed by other authors and has less error.
Vahid Nekoukhou, Ashkan Khalifeh, Eisa Mahmoudi,
Volume 13, Issue 2 (2-2020)
Abstract
In this paper, we study a three-parameter bivariate distribution obtained by taking Geometric minimum of Rayleigh distributions. Some important properties of this bivariate distribution have been investigated. It is observed that the maximum likelihood estimators of the parameters cannot be obtained in closed forms. We propose to use the EM algorithm to compute the maximum likelihood estimates of the parameters, and it is computationally quite tractable. Based on an extensive simulated study, the effectiveness of the proposed algorithm is confirmed. We also analyze one real data set for illustrative purposes. Finally, we conclude the paper.
Seyede Toktam Hosseini, Jafar Ahmadi,
Volume 14, Issue 2 (2-2021)
Abstract
In this paper, using the idea of inaccuracy measure in the information theory, the residual and past inaccuracy measures in the bivariate case are defined based on copula functions. Under the assumption of radial symmetry, the equality of these two criteria is shown, also by the equality between these two criteria, radially symmetrical models are characterized. A useful bound is provided by establishing proportional (inverse) hazard rate models for marginal distributions. Also, the proportional hazard rate model in bivariate mode is characterized by assuming proportionality between the introduced inaccuracy and its corresponding entropy. In addition, orthant orders are used to obtain inequalities. To illustrate the results, some examples and simulations are presented.
Mojtaba Esfahani, Mohammad Amini, Gholamreza Mohtashami Borzadaran,
Volume 15, Issue 1 (9-2021)
Abstract
In this article, the total time on test (TTT) transformation and its major properties are investigated. Then, the relationship between the TTT transformation and some subjects in reliability theory is expressed. The TTT diagram is also drawn for some well-known lifetime distributions, and a real-data analysis is performed based on this diagram. A new distorted family of distributions is introduced using the distortion function. The statistical interpretation of the new life distribution from the perspective of reliability is provided, and its survival function is derived. Finally, a generalization of the Weibull distribution is introduced using a new distortion function. A real data analysis shows its superiority in fitting in comparison to the traditional Weibull model.
Abouzar Bazyari, Morad Alizadeh,
Volume 16, Issue 1 (9-2022)
Abstract
In this paper, the collective risk model of an insurance company with constant surplus initial and premium when the claims are distributed as Exponential distribution and process number of claims distributed as Poisson distribution is considered. It is supposed that the reinsurance is done based on excess loss, which in that insurance portfolio, the part of total premium is the share of the reinsurer. A general formula for computing the infinite time ruin probability in the excess loss reinsurance risk model is presented based on the classical ruin probability. The random variable of the total amount of reinsurer's insurer payment in the risk model of excess loss reinsurance is investigated and proposed explicit formulas for calculating the infinite time ruin probability in the risk model of excess loss reinsurance. Finally, the results are examined for Lindley and Exponential distributions with numerical data.
Dr. Abouzar Bazyari,
Volume 16, Issue 2 (3-2023)
Abstract
In this paper, the individual risk model of the insurance company with dependent claims is considered and assumes that the binary vector of random variables of claim sizes is independent. Also, they have a common joint distribution function. A recursive formula for infinite time ruin probability is obtained according to the initial reserve and joint probability density function of random variables of claim sizes using probability inequalities and the induction method. Some numerical examples and simulation studies are presented for checking the results related to the light-tailed bivariate Poisson, heavy-tailed Log-Normal and Pareto distributions. The results are compared for Farlie–Gambel–Morgenstern and bivariate Frank copula functions. The effect of claims with heavy-tailed distributions on the ruin probability is also investigated.
Dr. Abouzar Bazyari,
Volume 17, Issue 1 (9-2023)
Abstract
In the excess loss reinsurance risk model, the amount of insurance premium paid by the company is influential in the ruin of that company. In this paper, the premium function is presented based on the expected amount of total payments of the reinsurer to the assigning insurer, the constraint on this function is investigated, and for the claims with any arbitrary distribution, the contour plots are drawn and with presenting optimization algorithm, infinite time ruin probability function will be minimum for different values of initial capital and threshold value. Finally, the excess loss reinsurance risk model with non-exponential claims is considered, and the infinite time ruin probability is calculated with numerical examples.
Omid Kharazmi, Faezeh Shirazi-Niya,
Volume 19, Issue 2 (4-2025)
Abstract
In this paper, by considering the generalized chi-squared information and the relative generalized chi-squared information measures, discrete versions of these information measures are introduced. Then, generalizations of these information quantities based on their convexity property are presented. Some essential features of these new measures and their relationships are studied. Moreover, the performance of these new information measures is investigated for some well-known and widely used models in coding theory and thermodynamics, such as escort distributions and generalized escort distributions. Finally, two applications of the introduced discrete generalized chi-squared information measure are examined in the context of image quality assessment. In addition, the results obtained from the performance of these measures are compared with the performance of the critical metric, peak signal-to-noise ratio. It is shown that the generalized chi-squared divergence measure exhibits performance similar to the peak signal-to-noise ratio and can be used as an alternative metric.
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