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Journal articles on the topic 'Multivariate distributions'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Kopociński, Bolesław. "Multivariate negative binomial distributions generated by multivariate exponential distributions." Applicationes Mathematicae 25, no. 4 (1999): 463–72. http://dx.doi.org/10.4064/am-25-4-463-472.

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2

Eaton, Morris L., and Michael D. Perlman. "Concentration inequalities for multivariate distributions: I. multivariate normal distributions." Statistics & Probability Letters 12, no. 6 (1991): 487–504. http://dx.doi.org/10.1016/0167-7152(91)90004-b.

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3

Poston, Wendy L., Norman L. Johnson, Samuel Kotz, and N. Balakrishnan. "Discrete Multivariate Distributions." Technometrics 40, no. 2 (1998): 160. http://dx.doi.org/10.2307/1270659.

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4

Kafadar, Karen, Norman L. Johnson, Samuel Kotz, and N. Balakrishnan. "Discrete Multivariate Distributions." Journal of the American Statistical Association 92, no. 440 (1997): 1654. http://dx.doi.org/10.2307/2965453.

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5

Papageorgiou, H., N. L. Johnson, S. Kotz, and N. Balakrishnan. "Discrete Multivariate Distributions." Biometrics 54, no. 2 (1998): 795. http://dx.doi.org/10.2307/3109790.

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6

Davy, P. J., and J. C. W. Rayner. "Multivariate geometric distributions." Communications in Statistics - Theory and Methods 25, no. 12 (1996): 2971–87. http://dx.doi.org/10.1080/03610929608831881.

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7

Oakes, David. "Multivariate survival distributions." Journal of Nonparametric Statistics 3, no. 3-4 (1994): 343–54. http://dx.doi.org/10.1080/10485259408832593.

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8

Poston, Wendy L. "Discrete Multivariate Distributions." Technometrics 40, no. 2 (1998): 161–62. http://dx.doi.org/10.1080/00401706.1998.10485207.

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9

Gupta, Rameshwar D., and Donald St P. Richards. "Multivariate Liouville distributions." Journal of Multivariate Analysis 23, no. 2 (1987): 233–56. http://dx.doi.org/10.1016/0047-259x(87)90155-2.

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10

Cline, Daren B. H., and Sidney I. Resnick. "Multivariate subexponential distributions." Stochastic Processes and their Applications 42, no. 1 (1992): 49–72. http://dx.doi.org/10.1016/0304-4149(92)90026-m.

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11

Nelson, Peter R. "Discrete Multivariate Distributions." Journal of Quality Technology 31, no. 3 (1999): 355. http://dx.doi.org/10.1080/00224065.1999.11979937.

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12

Helemäe, H.-L., and E.-M. Tiit. "MULTIVARIATE MINIMAL DISTRIBUTIONS." Proceedings of the Estonian Academy of Sciences. Physics. Mathematics 45, no. 4 (1996): 317. http://dx.doi.org/10.3176/phys.math.1996.4.03.

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13

Sundt, Bjørn. "On Multivariate Vernic Recursions." ASTIN Bulletin 30, no. 1 (2000): 111–22. http://dx.doi.org/10.2143/ast.30.1.504628.

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AbstractIn the present paper we extend a recursive algorithm developed by Vernic (1999) for compound distributions with bivariate counting distribution and univariate severity distributions to more general multivariate counting distributions.

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14

Jones, M. C. "Multivariate t and beta distributions associated with the multivariate F distribution." Metrika 54, no. 3 (2002): 215–31. http://dx.doi.org/10.1007/s184-002-8365-4.

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15

Sarabia, José María, Vanesa Jordá, Faustino Prieto, and Montserrat Guillén. "Multivariate Classes of GB2 Distributions with Applications." Mathematics 9, no. 1 (2020): 72. http://dx.doi.org/10.3390/math9010072.

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The general beta of the second kind distribution (GB2) is a flexible distribution which includes several relevant parametric families of distributions. This distribution has important applications in earnings and income distributions, finance and insurance. In this paper, several multivariate classes of the GB2 distribution are proposed. The different multivariate versions are based on two simple univariate representations of the GB2 distribution. The first type of multivariate distributions are constructed from a stochastic dependent representations defined in terms of gamma random variables.

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16

Minami, Mihoko. "Multivariate inverse Gaussian distribution as a limit of multivariate waiting time distributions." Journal of Statistical Planning and Inference 137, no. 11 (2007): 3626–33. http://dx.doi.org/10.1016/j.jspi.2007.03.038.

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17

Rootzén, Holger, and Nader Tajvidi. "Multivariate generalized Pareto distributions." Bernoulli 12, no. 5 (2006): 917–30. http://dx.doi.org/10.3150/bj/1161614952.

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18

Ghurye, S. G. "Some multivariate lifetime distributions." Advances in Applied Probability 19, no. 1 (1987): 138–55. http://dx.doi.org/10.2307/1427377.

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The family of distributions which have the lack-of-memory property is extended by incorporating simple patterns of ageing in the model. Some relatively simple multivariate distributions, obtained in this manner, might prove to be more realistic than distributions like the multivariate exponential.

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19

Jain, Kanchan, and Asok K. Nanda. "On multivariate weighted distributions." Communications in Statistics - Theory and Methods 24, no. 10 (1995): 2517–39. http://dx.doi.org/10.1080/03610929508831631.

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20

Bladt, Mogens, and Bo Friis Nielsen. "Multivariate Matrix-Exponential Distributions." Stochastic Models 26, no. 1 (2010): 1–26. http://dx.doi.org/10.1080/15326340903517097.

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21

Marshall, Albert W., and Ingram Olkin. "Families of Multivariate Distributions." Journal of the American Statistical Association 83, no. 403 (1988): 834–41. http://dx.doi.org/10.1080/01621459.1988.10478671.

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22

Ghurye, S. G. "Some multivariate lifetime distributions." Advances in Applied Probability 19, no. 01 (1987): 138–55. http://dx.doi.org/10.1017/s0001867800016426.

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The family of distributions which have the lack-of-memory property is extended by incorporating simple patterns of ageing in the model. Some relatively simple multivariate distributions, obtained in this manner, might prove to be more realistic than distributions like the multivariate exponential.

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23

Yeh, Hsiaw-Chan. "Multivariate semi-Weibull distributions." Journal of Multivariate Analysis 100, no. 8 (2009): 1634–44. http://dx.doi.org/10.1016/j.jmva.2009.01.015.

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24

Yeh, Hsiaw-Chan. "Multivariate semi-logistic distributions." Journal of Multivariate Analysis 101, no. 4 (2010): 893–908. http://dx.doi.org/10.1016/j.jmva.2009.09.002.

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25

Gupta, A. K., and F. C. Chang. "Multivariate skew-symmetric distributions." Applied Mathematics Letters 16, no. 5 (2003): 643–46. http://dx.doi.org/10.1016/s0893-9659(03)00060-0.

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26

Gupta, Rameshwar D., and Donald St P. Richards. "Multivariate Liouville distributions, III." Journal of Multivariate Analysis 43, no. 1 (1992): 29–57. http://dx.doi.org/10.1016/0047-259x(92)90109-s.

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27

Xia, Ming yuan. "ON SOME MULTIVARIATE DISTRIBUTIONS." Acta Mathematica Scientia 14 (1994): 88–90. http://dx.doi.org/10.1016/s0252-9602(18)30011-0.

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28

Arnold, Barry C., and Robert J. Beaver. "Some Skewed Multivariate Distributions." American Journal of Mathematical and Management Sciences 20, no. 1-2 (2000): 27–38. http://dx.doi.org/10.1080/01966324.2000.10737499.

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29

Gupta, R. D., and D. S. P. Richards. "Multivariate Liouville Distributions, IV." Journal of Multivariate Analysis 54, no. 1 (1995): 1–17. http://dx.doi.org/10.1006/jmva.1995.1042.

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30

Shatskikh, S. Ya. "Multivariate cauchy distributions as locally gaussian distributions." Journal of Mathematical Sciences 78, no. 1 (1996): 102–8. http://dx.doi.org/10.1007/bf02367960.

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31

Koehler, K. J., and J. T. Symanowski. "Constructing Multivariate Distributions with Specific Marginal Distributions." Journal of Multivariate Analysis 55, no. 2 (1995): 261–82. http://dx.doi.org/10.1006/jmva.1995.1079.

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32

Khokhlov, Yury, Victor Korolev, and Alexander Zeifman. "Multivariate Scale-Mixed Stable Distributions and Related Limit Theorems." Mathematics 8, no. 5 (2020): 749. http://dx.doi.org/10.3390/math8050749.

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In the paper, multivariate probability distributions are considered that are representable as scale mixtures of multivariate stable distributions. Multivariate analogs of the Mittag–Leffler distribution are introduced. Some properties of these distributions are discussed. The main focus is on the representations of the corresponding random vectors as products of independent random variables and vectors. In these products, relations are traced of the distributions of the involved terms with popular probability distributions. As examples of distributions of the class of scale mixtures of multiva

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33

Nadarajah, Saralees, and Samuel Kotz. "Sampling distributions associated with the multivariate t distribution." Statistica Neerlandica 59, no. 2 (2005): 214–34. http://dx.doi.org/10.1111/j.1467-9574.2005.00288.x.

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34

Nguyen, T. T. "Conditional Distributions and Characterizations of Multivariate Stable Distribution." Journal of Multivariate Analysis 53, no. 2 (1995): 181–93. http://dx.doi.org/10.1006/jmva.1995.1031.

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35

Okubo, Tomoya, and Shin-ichi Mayekawa. "Approximating score distributions using mixed-multivariate beta distribution." Behaviormetrika 44, no. 2 (2017): 369–84. http://dx.doi.org/10.1007/s41237-017-0019-7.

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36

SIRSI, SWARNAMALA. "SQUEEZING IN MULTIVARIATE SPIN SYSTEMS." International Journal of Modern Physics B 20, no. 11n13 (2006): 1465–75. http://dx.doi.org/10.1142/s0217979206034054.

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In contrast to the canonically conjugate variates q, p representing the position and momentum of a particle in the phase space distributions, the three Cartesian components, Jx, Jy, Jz of a spin-j system constitute the mutually non-commuting variates in the quasi-probabilistic spin distributions. It can be shown that a univariate spin distribution is never squeezed and one needs to look into either bivariate or trivariate distributions for signatures of squeezing. Several such distributions result if one considers different characteristic functions or moments based on various correspondence ru

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37

Pavithra, Celeste R., and T. G. Deepak. "Multivariate finite-support phase-type distributions." Journal of Applied Probability 57, no. 4 (2020): 1260–75. http://dx.doi.org/10.1017/jpr.2020.65.

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AbstractWe introduce a multivariate class of distributions with support I, a k-orthotope in $[0,\infty)^{k}$ , which is dense in the set of all k-dimensional distributions with support I. We call this new class ‘multivariate finite-support phase-type distributions’ (MFSPH). Though we generally define MFSPH distributions on any finite k-orthotope in $[0,\infty)^{k}$ , here we mainly deal with MFSPH distributions with support $[0,1)^{k}$ . The distribution function of an MFSPH variate is computed by using that of a variate in the MPH $^{*} $ class, the multivariate class of distributions introdu

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38

Savits, Thomas H. "A multivariate IFR class." Journal of Applied Probability 22, no. 1 (1985): 197–204. http://dx.doi.org/10.2307/3213759.

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A non-negative random vector T is said to have a multivariate increasing failure rate distribution (MIFR) if and only if E[h(x, T)] is log concave in x for all functions h(x, t) which are log concave in (x, t) and are non-decreasing and continuous in t for each fixed x. This class of distributions is closed under deletion, conjunction, convolution and weak limits. It contains the multivariate exponential distribution of Marshall and Olkin and those distributions having a log concave density. Also, it follows that if T is MIFR and ψ is non-decreasing, non-negative and concave then ψ (T) is IFR.

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39

Savits, Thomas H. "A multivariate IFR class." Journal of Applied Probability 22, no. 01 (1985): 197–204. http://dx.doi.org/10.1017/s0021900200029120.

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A non-negative random vector T is said to have a multivariate increasing failure rate distribution (MIFR) if and only if E[h(x, T)] is log concave in x for all functions h(x, t) which are log concave in (x, t) and are non-decreasing and continuous in t for each fixed x. This class of distributions is closed under deletion, conjunction, convolution and weak limits. It contains the multivariate exponential distribution of Marshall and Olkin and those distributions having a log concave density. Also, it follows that if T is MIFR and ψ is non-decreasing, non-negative and concave then ψ (T) is IFR.

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40

Kim, Hea-Jung. "A class of weighted multivariate distributions related to doubly truncated multivariate t-distribution." Statistics 44, no. 1 (2009): 89–106. http://dx.doi.org/10.1080/02331880902760645.

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41

Aly, Hanan M., and Ola A. Abuelamayem. "Multivariate Inverted Kumaraswamy Distribution: Derivation and Estimation." Mathematical Problems in Engineering 2020 (October 21, 2020): 1–27. http://dx.doi.org/10.1155/2020/6349523.

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Industrial revolution leads to the manufacturing of multicomponent products; to guarantee the sufficiency of the product and consumer satisfaction, the producer has to study the lifetime of the products. This leads to the use of bivariate and multivariate lifetime distributions in reliability engineering. The most popular and applicable is Marshall–Olkin family of distributions. In this paper, a new bivariate lifetime distribution which is the bivariate inverted Kumaraswamy (BIK) distribution is found and its properties are illustrated. Estimation using both maximum likelihood and Bayesian app

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42

Khokhlov, Yury, and Victor Korolev. "On a Multivariate Analog of the Zolotarev Problem." Mathematics 9, no. 15 (2021): 1728. http://dx.doi.org/10.3390/math9151728.

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A generalized multivariate problem due to V. M. Zolotarev is considered. Some related results on geometric random sums and (multivariate) geometric stable distributions are extended to a more general case of “anisotropic” random summation where sums of independent random vectors with multivariate random index having a special multivariate geometric distribution are considered. Anisotropic-geometric stable distributions are introduced. It is demonstrated that these distributions are coordinate-wise scale mixtures of elliptically contoured stable distributions with the Marshall–Olkin mixing dist

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43

Sheu, Shey-Huei, and William S. Griffith. "Multivariate imperfect repair." Journal of Applied Probability 29, no. 4 (1992): 947–56. http://dx.doi.org/10.2307/3214726.

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We consider models of systems of components with dependent life-lengths having certain multivariate distributions. Upon failure, components are repaired. Two types of repair are possible. After perfect repair, a unit has the same life distribution as a new item. After imperfect repair, a unit has the life distribution of an item which is of the same age but has never failed. Different sources of failure are distinguished and affect the probabilities of perfect and imperfect repair.

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44

Sheu, Shey-Huei, and William S. Griffith. "Multivariate imperfect repair." Journal of Applied Probability 29, no. 04 (1992): 947–56. http://dx.doi.org/10.1017/s0021900200043813.

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We consider models of systems of components with dependent life-lengths having certain multivariate distributions. Upon failure, components are repaired. Two types of repair are possible. After perfect repair, a unit has the same life distribution as a new item. After imperfect repair, a unit has the life distribution of an item which is of the same age but has never failed. Different sources of failure are distinguished and affect the probabilities of perfect and imperfect repair.

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45

Albrecher, Hansjörg, Martin Bladt, and Mogens Bladt. "Multivariate fractional phase–type distributions." Fractional Calculus and Applied Analysis 23, no. 5 (2020): 1431–51. http://dx.doi.org/10.1515/fca-2020-0071.

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Abstract We extend the Kulkarni class of multivariate phase–type distributions in a natural time–fractional way to construct a new class of multivariate distributions with heavy-tailed Mittag-Leffler(ML)-distributed marginals. The approach relies on assigning rewards to a non–Markovian jump process with ML sojourn times. This new class complements an earlier multivariate ML construction [2] and in contrast to the former also allows for tail dependence. We derive properties and characterizations of this class, and work out some special cases that lead to explicit density representations.

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46

Sarkar, Sanat K., Kai-Tai Fang, Samuel Kotz, and Kai-Wang Ng. "Symmetric Multivariate and Related Distributions." Journal of the American Statistical Association 86, no. 416 (1991): 1144. http://dx.doi.org/10.2307/2290544.

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47

Hong, Chong Sun, and Jae Young Kim. "Multivariate CTE for copula distributions." Journal of the Korean Data and Information Science Society 28, no. 2 (2017): 421–33. http://dx.doi.org/10.7465/jkdi.2017.28.2.421.

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48

Lee, Myoung-jae. "Probability inequalities in multivariate distributions." Econometric Reviews 18, no. 4 (1999): 387–415. http://dx.doi.org/10.1080/07474939908800352.

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49

MADAN, DILIP B. "MULTIVARIATE DISTRIBUTIONS FOR FINANCIAL RETURNS." International Journal of Theoretical and Applied Finance 23, no. 06 (2020): 2050041. http://dx.doi.org/10.1142/s0219024920500417.

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Multivariate return distributions consistent with bilateral gamma marginals are formulated and termed multivariate bilateral gamma (MBG). Tail probability distances and Wasserstein distances between return data, model simulations and their squares evaluate the model performance. A full Gaussian copula [Formula: see text] is taken as an alternate test model and the MBG delivers a comparatively better performance for equity pairs. The MBG is however inadequate for the S&P 500 index return when paired with the VIX returns. Applying MBG to the S&P 500 the index and regression residuals of

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50

Mosler, Karl, and Gleb Koshevoy. "Zonoid trimming for multivariate distributions." Annals of Statistics 25, no. 5 (1997): 1998–2017. http://dx.doi.org/10.1214/aos/1069362382.

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