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Journal articles on the topic 'And Distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Omer, Abdeen. "Medicines Distribution, Regulatory Privatisation, Social Welfare Services and Financing Alternatives." International Journal of Medical Reviews and Case Reports 2, Reports in Surgery and Dermatolo (2018): 1. http://dx.doi.org/10.5455/ijmrcr.medicine-distributions-sudan.

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2

Panton, Don B. "Distribution function values for logstable distributions." Computers & Mathematics with Applications 25, no. 9 (May 1993): 17–24. http://dx.doi.org/10.1016/0898-1221(93)90128-i.

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3

Guo, Ran, and Jiulin Du. "Are power-law distributions an equilibrium distribution or a stationary nonequilibrium distribution?" Physica A: Statistical Mechanics and its Applications 406 (July 2014): 281–86. http://dx.doi.org/10.1016/j.physa.2014.03.056.

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4

Gómez, Héctor W., Osvaldo Venegas, and Heleno Bolfarine. "Skew-symmetric distributions generated by the distribution function of the normal distribution." Environmetrics 18, no. 4 (2007): 395–407. http://dx.doi.org/10.1002/env.817.

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5

Cui, Hong Jun, Xiao Gao Xing, Guo Feng Liang, and Ya Ping Li. "Prediction Method for Freeway Wheelpath Distribution." Applied Mechanics and Materials 409-410 (September 2013): 1347–52. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1347.

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According to the analysis of distributing characteristics which obtained by the investigation of different types vehicles of many freeways, the law is discovered that distributing characteristics of same types of vehicles are similar on different freeways, using test to certificate that distributions of same types of vehicles are submitted to identical normal distribution, and the rationality of this phenomenon is discussed with statistical point of view. Calculated and predicted method of wheelpath distribution and axle load distribution are established that based on traffic flow. In the end, an example is used to confirm the accuracy of this method by contrasting the survey result and the predicted result.

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6

Çakmakyapan, Selen, and Gamze Özel Kadılar. "A New Customer Lifetime Duration Distribution: The Kumaraswamy Lindley Distribution." International Journal of Trade, Economics and Finance 5, no. 5 (October 2014): 441–44. http://dx.doi.org/10.7763/ijtef.2014.v5.412.

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7

Moya-Cessa, J. R., H. Moya-Cessa, L. R. Berriel-Valdos, O. Aguilar-Loreto, and P. Barberis-Blostein. "Unifying distribution functions: some lesser known distributions." Applied Optics 47, no. 22 (April 24, 2008): E13. http://dx.doi.org/10.1364/ao.47.000e13.

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8

Deshmukh, S. R., and M. S. Kasture. "BIVARIATE DISTRIBUTION WITH TRUNCATED POISSON MARGINAL DISTRIBUTIONS." Communications in Statistics - Theory and Methods 31, no. 4 (May 14, 2002): 527–34. http://dx.doi.org/10.1081/sta-120003132.

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9

Li, Kaican, and Zhi Geng. "The Noncentral Wishart Distribution and Related Distributions." Communications in Statistics - Theory and Methods 32, no. 1 (January 3, 2003): 33–45. http://dx.doi.org/10.1081/sta-120017798.

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10

Cousineau, Denis, Jean-Philippe Thivierge, Bradley Harding, and Yves Lacouture. "Constructing a group distribution from individual distributions." Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 70, no. 3 (2016): 253–77. http://dx.doi.org/10.1037/cep0000069.

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11

Castillo, E., and J. Galambos. "Conditional distributions and the bivariate normal distribution." Metrika 36, no. 1 (December 1989): 209–14. http://dx.doi.org/10.1007/bf02614094.

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12

Voit, Eberhard O., and Shuiyang Yu. "The S-Distribution: Approximation of Discrete Distributions." Biometrical Journal 36, no. 2 (1994): 205–19. http://dx.doi.org/10.1002/bimj.4710360215.

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13

Jawade, Sakshi Dinesh, and Dinesh V. Rojatkar. "Power Distribution - A Challange." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 790–93. http://dx.doi.org/10.31142/ijtsrd4657.

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14

VĂDUVA, Ion. "A PARTICULAR LIFETIME DISTRIBUTION." Review of the Air Force Academy 15, no. 2 (October 20, 2017): 5–14. http://dx.doi.org/10.19062/1842-9238.2017.15.2.1.

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15

Taurino, Giulia. "Distributing CanCon: CBC strategies for international distribution." Journal of Popular Television 8, no. 3 (October 1, 2020): 299–305. http://dx.doi.org/10.1386/jptv_00029_1.

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This article tackles the evolution of Canadian Broadcasting Corporation international distribution strategies at the intersection of the contemporary television landscape, by providing a context and definition for Canadian content (CanCon) rules, so as to consider more recent debates on the positioning of foreign streaming services in Canada in relation to existing broadcasting companies. The aim is to problematize media policies, by outlining the present state of the debate and updating the conversation to include global streaming TV players. Key questions are explored, such as whether CanCon rules are outdated forms of cultural protectionism or still represent viable answers to the risks of media imperialism.

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16

Castro-Manzano, J. Martín. "Distribution Tableaux, Distribution Models." Axioms 9, no. 2 (April 17, 2020): 41. http://dx.doi.org/10.3390/axioms9020041.

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The concept of distribution is a concept within traditional logic that has been fundamental for the syntactic development of Sommers and Englebretsen’s term functor logic, a logic that recovers the term syntax of traditional logic. The issue here, however, is that the semantic counterpart of distribution for this logic is still in the making. Consequently, given this disparity between syntax and semantics, in this contribution we adapt some ideas of term functor logic tableaux to develop models of distribution, thus providing some alternative formal semantics to help close this breach.

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17

Rahmouni, Mohieddine, and Ayman Orabi. "The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution." International Journal of Statistics and Probability 7, no. 1 (November 2, 2017): 1. http://dx.doi.org/10.5539/ijsp.v7n1p1.

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This paper introduces a new two-parameter lifetime distribution, called the exponential-generalized truncated geometric (EGTG) distribution, by compounding the exponential with the generalized truncated geometric distributions. The new distribution involves two important known distributions, i.e., the exponential-geometric (Adamidis and Loukas, 1998) and the extended (complementary) exponential-geometric distributions (Adamidis et al., 2005; Louzada et al., 2011) in the minimum and maximum lifetime cases, respectively. General forms of the probability distribution, the survival and the failure rate functions as well as their properties are presented for some special cases. The application study is illustrated based on two real data sets.

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18

Osowole, Oyedeji Isola, Ngozi Nzelu, and Rita Nwaka. "Another Statistical Distribution: The Exponentiated Complementary Mukherjee-Islam Distribution." European Journal of Engineering and Technology Research 6, no. 1 (January 25, 2021): 118–23. http://dx.doi.org/10.24018/ejers.2021.6.1.2028.

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This study considered a newly proposed Exponentiated Complementary Mukherjee-Islam distribution obtained by exponentiating the Complementary Mukherjee-Islam distribution. Some properties of the new distribution were derived and results from the distribution indicated that the distribution is a better alternative than its baseline distribution. The new distribution is therefore a creditable addition to the existing family of exponentiated distributions.

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19

Khmaladze, Estate. "Distribution free testing for conditional distributions given covariates." Statistics & Probability Letters 129 (October 2017): 348–54. http://dx.doi.org/10.1016/j.spl.2017.06.026.

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20

Boyd, Albert V. "Fitting the Truncated Pareto Distribution to Loss Distributions." Journal of the Staple Inn Actuarial Society 31 (March 1988): 151–58. http://dx.doi.org/10.1017/s2049929900010291.

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Hogg and Klugman use the truncated Pareto distribution with probability density functionwhere δ≥0 is specified and α > 0 and λ > 0 are unknown parameters, to describe insurance claims. This is fitted first of all by the method of moments, using the estimatorsand where is the mean of a simple random sample, and the (biased) varianceThe authors then suggest, on pp. 113–16, that these estimates be used as starting values in a Newton iteration to get the maximum likelihood estimates of the parameters, but this technique can fail as a result of convergence problems. The object of this note is to show that this has led Hogg and Klugman to underestimate seriously the area in the tail of a fitted loss distribution, and to discuss a method of circumventing this difficulty.

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21

Pudovkin, Alexander I., and Lutz Bornmann. "Approximation of citation distributions to the Poisson distribution." COLLNET Journal of Scientometrics and Information Management 12, no. 1 (January 2, 2018): 49–53. http://dx.doi.org/10.1080/09737766.2017.1332605.

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22

Liu, Tong, Ping Zhang, Wu-Sheng Dai, and Mi Xie. "An intermediate distribution between Gaussian and Cauchy distributions." Physica A: Statistical Mechanics and its Applications 391, no. 22 (November 2012): 5411–21. http://dx.doi.org/10.1016/j.physa.2012.06.035.

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23

Schatzki, Thomas F. "Distribution of Aflatoxin in Pistachios. 1. Lot Distributions." Journal of Agricultural and Food Chemistry 43, no. 6 (June 1995): 1561–65. http://dx.doi.org/10.1021/jf00054a027.

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24

Khmaladze, Estate. "Note on distribution free testing for discrete distributions." Annals of Statistics 41, no. 6 (December 2013): 2979–93. http://dx.doi.org/10.1214/13-aos1176.

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25

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

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26

Hady, Dina H. Abdel. "USE OF EXPONENTIAL DISTRIBUTION FOR HYBRIDIZATION OF DISTRIBUTIONS." Advances and Applications in Statistics 58, no. 1 (September 20, 2019): 57–75. http://dx.doi.org/10.17654/as058010057.

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27

Steliga, Katarzyna, and Dominik Szynal. "On counting distributions related to the Delaporte distribution." Applicationes Mathematicae 46, no. 1 (2019): 1–38. http://dx.doi.org/10.4064/am2337-8-2018.

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28

Dahel, S., and N. Giri. "Some distributions related to a noncentral wishart distribution." Communications in Statistics - Theory and Methods 23, no. 1 (January 1994): 229–37. http://dx.doi.org/10.1080/03610929408831249.

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29

Ahmadabadi, M. Nili, Y. Farjami, and M. B. Moghadam. "Approximating Distributions by Extended Generalized Lambda Distribution (XGLD)." Communications in Statistics - Simulation and Computation 41, no. 1 (January 2012): 1–23. http://dx.doi.org/10.1080/03610911003681503.

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30

Newhall, Bruce, and Juan Arvelo. "Relating the distribution of bathymetry to clutter distributions." Journal of the Acoustical Society of America 118, no. 3 (September 2005): 2041. http://dx.doi.org/10.1121/1.4785846.

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31

Cirillo, Pasquale, Frank Redig, and Wioletta Ruszel. "Duality and stationary distributions of wealth distribution models." Journal of Physics A: Mathematical and Theoretical 47, no. 8 (February 10, 2014): 085203. http://dx.doi.org/10.1088/1751-8113/47/8/085203.

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32

C M, Latha, and Sandhya E. "CG and CEG Distributions with Uniform Secondary Distribution." International Journal of Scientific and Research Publications (IJSRP) 9, no. 12 (December 6, 2019): p9635. http://dx.doi.org/10.29322/ijsrp.9.12.2019.p9635.

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33

Okubo, Tomoya, and Shin-ichi Mayekawa. "Approximating score distributions using mixed-multivariate beta distribution." Behaviormetrika 44, no. 2 (March 20, 2017): 369–84. http://dx.doi.org/10.1007/s41237-017-0019-7.

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34

Gharib, M. "Characterizations of the exponential distribution via mixing distributions." Microelectronics Reliability 36, no. 3 (March 1996): 293–305. http://dx.doi.org/10.1016/0026-2714(95)00131-x.

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35

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

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36

Cao, Quang V., and Qinglin Wu. "Characterizing wood fiber and particle length with a mixture distribution and a segmented distribution." Holzforschung 61, no. 2 (March 1, 2007): 124–30. http://dx.doi.org/10.1515/hf.2007.023.

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Abstract The length data from 12 samples of wood fibers and particles were described using lognormal and Weibull distributions. While both distributions fitted the middle range of the data well, the lognormal distribution provided a closer fit for short fibers and particles and the Weibull distribution was more appropriate for long ones. A mixture of the lognormal and Weibull distributions was developed using a variable weight to allow the new distribution to take the lognormal form for short fibers and gradually change to the Weibull form for long fibers. In the segmented distribution approach, a left segment of the lognormal distribution was joined to a right segment from the Weibull form. The Anderson-Darling goodness-of-fit test at the 5% level failed to reject the hypothesis that the mixture distribution and the segmented distribution fitted the data. Q-Q plots showed that both the mixture and segmented distributions provided an excellent fit to the fiber and particle length data, combining the best features of the lognormal and the Weibull distributions. These two new distributions are therefore better alternatives than the single lognormal and Weibull distributions for this data set.

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37

So, Jacky C. "The Distribution of Financial Ratios—A Note." Journal of Accounting, Auditing & Finance 9, no. 2 (April 1994): 215–23. http://dx.doi.org/10.1177/0148558x9400900205.

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Three competitive distributions are offered by the literature to explain the non-normality and skewness of the cross-sectional distribution of financial ratios: the mixture of normal distributions, the lognormal distribution, and the gamma distribution. Using a new technique, this paper shows that the lognormal distribution and the gamma distribution are not supported by the empirical evidence. Although these two distributions indeed capture skewness, they do not portray the correct shape of the distributions. The non-normal stable Paretian distribution seems to be good candidate to describe the distribution of financial ratios.

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38

GARCÍA, CATALINA BEATRIZ GARCÍA, JOSÉ GARCÍA PÉREZ, and SALVADOR CRUZ RAMBAUD. "THE GENERALIZED BIPARABOLIC DISTRIBUTION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, no. 03 (June 2009): 377–96. http://dx.doi.org/10.1142/s0218488509005930.

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Beta distributions have been applied in a variety of fields in part due to its similarity to the normal distribution while allowing for a larger flexibility of skewness and kurtosis coverage when compared to the normal distribution. In spite of these advantages, the two-sided power (TSP) distribution was presented as an alternative to the beta distribution to address some of its short-comings, such as not possessing a cumulative density function (cdf) in a closed form and a difficulty with the interpretation of its parameters. The introduction of the biparabolic distribution and its generalization in this paper may be thought of in the same vein. Similar to the TSP distribution, the generalized biparabolic (GBP) distribution also possesses a closed form cdf, but contrary to the TSP distribution its density function is smooth at the mode. We shall demonstrate, using a moment ratio diagram comparison, that the GBP distribution provides for a larger flexibility in skewness and kurtosis coverage than the beta distribution when restricted to the unimodal domain. A detailed mean-variance comparison of GBP, beta and TSP distributions is presented in a Project Evaluation and Review Technique (PERT) context. Finally, we shall fit a GBP distribution to an example of financial European stock data and demonstrate a favorable fit of the GBP distribution compared to other distributions that have traditionally been used in that field, including the beta distribution.

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39

Menon, V. J., and D. C. Agrawal. "Maxwellian distribution versus Rayleigh distribution." American Journal of Physics 54, no. 11 (November 1986): 1034–35. http://dx.doi.org/10.1119/1.14819.

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40

Rovira, Joan R. "Piketty on Growth and Distribution." Revista Hacienda Pública Espñola 214, no. 4 (September 2015): 91–114. http://dx.doi.org/10.7866/hpe-rpe.15.3.4.

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41

NK, Sferopoulos. "Anatomical Distribution of Intramuscular Lipomas." Journal of Forensic Science and Research 1, no. 1 (2017): 035–39. http://dx.doi.org/10.29328/journal.jfsr.1001004.

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42

Kishi, Yasuhito, and Issei Kino. "CLOSED FORM OF PH-DISTRIBUTION." Journal of the Operations Research Society of Japan 49, no. 2 (2006): 98–116. http://dx.doi.org/10.15807/jorsj.49.98.

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43

Sýkorová, A., E. Šárka, Z. Bubník, M. Schejbal, and P. Dostálek. "Size distribution of barley kernels." Czech Journal of Food Sciences 27, No. 4 (September 9, 2009): 249–58. http://dx.doi.org/10.17221/26/2009-cjfs.

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Barley primarily serves as a major animal feed crop; smaller amounts of barley are used in health foods and in the malting process. Detailed geometric parameters of kernels are very important for the design of food engineering processes, such as the air transport, drying, milling, and malting. Image analysis was used to determine the size parameters of one hundred kernels of selected varieties of <I>Hordeum vulgare</I> L. The data for every kernel captured were stored for further use, together with the mean, standard deviation (SD), coefficient of variation (<I>CV</I>), and images themselves. The measured data were then used to compute the volume and surface area of each of the five kernel models (Models 0–4), the results being subsequently verified by pycnometric measurement. Model 0 represents the general ellipsoid, models 1–3 various combinations of two parts of a general ellipsoid with one or two cone frustums. The best fitted model 4 was a combination of two cone frustums. Based on the results of image analysis measurements and on the presented model 4, a simplified method for the specific surface estimation of barley grains from the weight of 1000 kernels is recommended.

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44

Loukas, Sotirios, and H. Papageorgiou. "On a trivariate Poisson distribution." Applications of Mathematics 36, no. 6 (1991): 432–39. http://dx.doi.org/10.21136/am.1991.104480.

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45

Martello, Gian Vittorio, and Fabrizio Gabbiani. "Biomass distribution in desmid communities." Algological Studies 154 (August 21, 2018): 1–39. http://dx.doi.org/10.1127/algol_stud/2018/0288.

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46

Unami, Koichi, Macarius Yangyuoru, Toshihiko Kawachi, Samuel G. K. Adiku, and Maho Iwaki. "Identification of Drifting Rainfall Distribution." Journal of Rainwater Catchment Systems 12, no. 2 (2007): 23–28. http://dx.doi.org/10.7132/jrcsa.kj00004557611.

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47

Pyanylo, Ya, and V. Sobko. "Pipeline pressure distribution finding methods." Mathematical Modeling and Computing 3, no. 2 (December 31, 2016): 199–207. http://dx.doi.org/10.23939/mmc2016.02.199.

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48

Gupta, Arjun K., and Daya K. Nagar. "Matrix-variate beta distribution." International Journal of Mathematics and Mathematical Sciences 24, no. 7 (2000): 449–59. http://dx.doi.org/10.1155/s0161171200002398.

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We propose matrix-variate beta type III distribution. Several properties of this distribution including Laplace transform, marginal distribution and its relationship with matrix-variate beta type I and type II distributions are also studied.

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49

Podlaski, Rafał. "Characterization of diameter distribution data in near-natural forests using the Birnbaum–Saunders distribution." Canadian Journal of Forest Research 38, no. 3 (March 2008): 518–27. http://dx.doi.org/10.1139/x07-190.

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The purpose of this study is to investigate the suitability of the Birnbaum–Saunders distribution to model diameter at breast height (DBH) distributions of near-natural complex structure silver fir ( Abies alba Mill.) – European beech ( Fagus sylvatica L.) forests. The investigations were carried out in Świętokrzyski National Park, situated in Central Poland. To estimate the parameters of the Birnbaum–Saunders distribution, three methods were used: the maximum likelihood method (MLE) and the mean–mean estimator, the modified moment method (MME), and the graphical method (GME). The empirical DBH distributions in near-natural fir–beech stands, which arose according to the model taking into account the overlapping of fir mortality and beech regeneration, were generally conformed to the Birnbaum–Saunders distribution. In such forests, the Birnbaum–Saunders distribution approximated the empirical DBH distributions more precisely than the Weibull and gamma distributions.

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50

O’Kane, Dominic. "Approximating independent loss distributions with an adjusted binomial distribution." Journal of Credit Risk 7, no. 4 (December 2011): 103–17. http://dx.doi.org/10.21314/jcr.2012.133.

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