Relevant bibliographies by topics / Farlie-Gumbel-Morgenstern family of bivariate distributions

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Academic literature on the topic 'Farlie-Gumbel-Morgenstern family of bivariate distributions'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Farlie-Gumbel-Morgenstern family of bivariate distributions"

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El-Morshedy, M., Fahad Sameer Alshammari, Yasser S. Hamed, Mohammed S. Eliwa, and Haitham M. Yousof. "A New Family of Continuous Probability Distributions." Entropy 23, no. 2 (2021): 194. http://dx.doi.org/10.3390/e23020194.

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In this paper, a new parametric compound G family of continuous probability distributions called the Poisson generalized exponential G (PGEG) family is derived and studied. Relevant mathematical properties are derived. Some new bivariate G families using the theorems of “Farlie-Gumbel-Morgenstern copula”, “the modified Farlie-Gumbel-Morgenstern copula”, “the Clayton copula”, and “the Renyi’s entropy copula” are presented. Many special members are derived, and a special attention is devoted to the exponential and the one parameter Pareto type II model. The maximum likelihood method is used to e
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Refaie, Mohamed. "A New Family of Continuous Distributions: Properties, Copulas and Real Life Data Modeling." Statistics, Optimization & Information Computing 9, no. 3 (2021): 748–68. http://dx.doi.org/10.19139/soic-2310-5070-1130.

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A new family of distributions called the Kumaraswamy Rayleigh family is defied and studied. Some of its relevant statistical properties are derived. Many new bivariate type G families using the of Farlie-Gumbel-Morgenstern, modified Farlie-Gumbel-Morgenstern copula, Clayton copula and Renyi’s entropy copula are derived. The method of the maximum likelihood estimation is used. Some special models based on log-logistic, exponential, Weibull, Rayleigh, Pareto type II and Burr type X, Lindley distributions are presented and studied. Three dimensional skewness and kurtosis plots are presented. A gr
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Gupta, Ramesh C. "Reliability characteristics of Farlie–Gumbel–Morgenstern family of bivariate distributions." Communications in Statistics - Theory and Methods 45, no. 8 (2015): 2342–53. http://dx.doi.org/10.1080/03610926.2013.828075.

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Attwa, Rasha Abd-Elwahaab, Taha Radwan, and Esraa Osama Abo Zaid. "Bivariate q-extended Weibull morgenstern family and correlation coefficient formulas for some of its sub-models." AIMS Mathematics 8, no. 11 (2023): 25325–42. http://dx.doi.org/10.3934/math.20231292.

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<abstract><p>A bivariate extension of a flexible univariate family is proposed. The new family is called bivariate q-extended Weibull Morgenstern family of distributions which can be constructed based on the Farlie-Gumbel-Morgenstern (FGM) copula technique. After introducing the new family, four sub-models are discussed in detail from the theoretical and numerical coefficient of correlation point of view with pointing to the effect of the $ q $ parameter.</p></abstract>
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Shrahili, Mansour, and Naif Alotaibi. "A New Parametric Life Family of Distributions: Properties, Copula and Modeling Failure and Service Times." Symmetry 12, no. 9 (2020): 1462. http://dx.doi.org/10.3390/sym12091462.

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A new family of probability distributions is defined and applied for modeling symmetric real-life datasets. Some new bivariate type G families using Farlie–Gumbel–Morgenstern copula, modified Farlie–Gumbel–Morgenstern copula, Clayton copula and Renyi’s entropy copula are derived. Moreover, some of its statistical properties are presented and studied. Next, the maximum likelihood estimation method is used. A graphical assessment based on biases and mean squared errors is introduced. Based on this assessment, the maximum likelihood method performs well and can be used for estimating the model pa
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Cuadras, Carles M., and Walter Díaz. "Another generalization of the bivariate FGM distribution with two-dimensional extensions." Acta et Commentationes Universitatis Tartuensis de Mathematica 16, no. 1 (2012): 3–12. http://dx.doi.org/10.12697/acutm.2012.16.01.

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The Farlie–Gumbel–Morgenstern family of bivariate distributions with given marginals is frequently used in theory and applications and has been generalized in many ways. With the help of two auxiliary distributions, we propose another generalization and study its properties. After defining the dimension of a distribution as the cardinal of the set of canonical correlations, we prove that some well-known distributions are practically two-dimensional. Then we introduce an extended FGM family in two dimensions and study how to approximate any distribution to this family.
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Ahmadi, Jafar, and M. Fashandi. "Characterization of symmetric distributions based on concomitants of ordered variables from FGMs family of bivariate distributions." Filomat 33, no. 13 (2019): 4239–50. http://dx.doi.org/10.2298/fil1913239a.

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Several characterization results of a symmetric distribution based on concomitants of order statistics as well as k-records from Farlie-Gumbel-Morgenstern (FGM) family of bivariate distributions are established. These include characterizations of a symmetric distribution on the basis of equality in distribution, moments, R?nyi and Tsallis entropies of concomitants of upper and lower order statistics, also in terms of the same properties of concomitants of upper and lower k-records.
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Cuadras, Carles M. "On Bivariate Distributions with Singular Part." Axioms 13, no. 7 (2024): 433. http://dx.doi.org/10.3390/axioms13070433.

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There are many families of bivariate distributions with given marginals. Most families, such as the Farlie–Gumbel–Morgenstern (FGM) and the Ali–Mikhail–Haq (AMH), are absolutely continuous, with an ordinary probability density. In contrast, there are few families with a singular part or a positive mass on a curve. We define a general condition useful to detect the singular part of a distribution. By continuous extension of the bivariate diagonal expansion, we define and study a wide family containing these singular distributions, obtain the probability density, and find the canonical correlati
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Alawady, M. A., H. M. Barakat, Shengwu Xiong, and M. A. Abd Elgawad. "On concomitants of dual generalized order statistics from Bairamov–Kotz–Becki Farlie–Gumbel–Morgenstern bivariate distributions." Asian-European Journal of Mathematics 14, no. 10 (2021): 2150185. http://dx.doi.org/10.1142/s1793557121501850.

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In this paper, we study the concomitants of [Formula: see text]-dual generalized order statistics ([Formula: see text]-DGOS) from Bairamov–Kotz–Becki Farlie–Gumbel–Morgenstern bivariate distributions as an extension of several recent papers. This study can also be applied to the model of [Formula: see text]-generalized order statistics ([Formula: see text]-GOS) as a parallel model of [Formula: see text]-DGOS. Furthermore, the joint distribution of [Formula: see text]-DGOS of concomitants for this family is studied. Some useful recurrence relations between single and product moments of concomit
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Husseiny, Islam A., Metwally A. Alawady, Salem A. Alyami, and Mohamed A. Abd Elgawad. "Measures of Extropy Based on Concomitants of Generalized Order Statistics under a General Framework from Iterated Morgenstern Family." Mathematics 11, no. 6 (2023): 1377. http://dx.doi.org/10.3390/math11061377.

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In this work, we reveal some distributional characteristics of concomitants of generalized order statistics (GOS) with parameters that are pairwise different, arising from iterated Farlie–Gumbel–Morgenstern (IFGM) family of bivariate distributions. Additionally, the joint distribution and product moments of concomitants of GOS for this family are discussed. Moreover, some well-known information measures, i.e., extropy, cumulative residual extropy (CRJ), and negative cumulative extropy (NCJ), are derived. Applications of these results are given for order statistics, record values, and progressi
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Conference papers on the topic "Farlie-Gumbel-Morgenstern family of bivariate distributions"

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Qu, Xiuli, and Jing Shi. "Characterizing Wind Speed and Air Density for Wind Energy Estimation." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-13059.

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Wind energy is the fastest growing renewable energy source in the past decade. To estimate the wind energy potential for a specific site, the long-term wind data need to be analyzed and accurately modeled. Wind speed and air density are the two key parameters for wind energy potential calculation, and their characteristics determine the long-term wind energy estimation. In this paper, we analyze the wind speed and air density data obtained from two observation sites in North Dakota and Colorado, and the variations of wind speed and air density in long term are demonstrated. We obtain univariat
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