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Journal articles on the topic 'Distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 November 2024

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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

Maity, Mahuya, and Papiya Saha. "Normal Distribution." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 298–99. http://dx.doi.org/10.21275/sr231126211340.

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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

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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8

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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9

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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10

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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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

Ejsmont, Wiktor. "Free Meixner Distributions." Didactics of Mathematics 13, no. 17 (2016): 13–16. http://dx.doi.org/10.15611/dm.2016.13.02.

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14

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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15

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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16

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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17

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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18

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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19

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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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

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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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

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

Tomaselli, Domenico, Paul Stursberg, Michael Metzger, and Florian Steinke. "Learning probability distributions over georeferenced distribution grid models." Electric Power Systems Research 235 (October 2024): 110636. http://dx.doi.org/10.1016/j.epsr.2024.110636.

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32

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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33

Njamen Njomen, Didier Alain, Thiery Donfack, Joseph Ngatchou-Wandji, and Georges Nguefack-Tsague. "E-Bayesian Estimation under Loss Functions in Competing Risks." European Journal of Pure and Applied Mathematics 15, no. 2 (April 30, 2022): 753–73. http://dx.doi.org/10.29020/nybg.ejpam.v15i2.4351.

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Using gamma prior distribution of which shape hyperparameter has beta distributio and rate parameter has three different distributions over a finite interval, we studied the E-Bayesian estimation of one scale parameter of Gompertz distribution based on progressively type I censored sample from the competing risks model subject to K independent causes. The estimators obtainedgeneralize those issued from the quadratic loss, entropy loss and DeGroot loss functions.

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34

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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35

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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36

Singh Kachhwaha, Jaideep. "COVID-19 Vaccine Distribution." International Journal of Science and Research (IJSR) 12, no. 2 (February 5, 2023): 254–55. http://dx.doi.org/10.21275/sr23202160226.

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37

Xaydarovich, Raimkulov Iskandar, Jabborov Giyosjon Gafforjonovich, Davlatov Ravshan Berdiyevich, and Xadjayeva Nigora Jurakulovna. "DISTRIBUTION OF CHICKEN ECTOPARASITES." American Journal of Veterinary Sciences and Wildlife Discovery 6, no. 3 (May 1, 2024): 17–20. http://dx.doi.org/10.37547/tajvswd/volume06issue03-04.

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This article presents literature data and results of preliminary research about ectoparasites that are widespread and cause great economic damage in chicken coops and some poultry farms. Their biology, distribution, clinical signs, epizootology and preventive measures are presented.

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38

Ilori, Adetunji K., Omaku P. Enesi, Kole Emmanuel, Dayo V. Kayode, and Adebisi Michael. "Weighted Rayleigh Distribution." International Journal of Research and Innovation in Applied Science IX, no. VIII (2024): 323–36. http://dx.doi.org/10.51584/ijrias.2024.908030.

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This paper introduces the Weighted Rayleigh (WR) distribution by inducing inverted weight function into the existing Rayleigh distribution. Statistical and mathematical expressions of its properties such as Survival Function, Hazard Function, Moments, Moment Generating Function, Mean Deviation and Renyi entropy were explicitly derived. The model’s parameter was estimated using maximum likelihood method of estimation. Two real life data sets on cancer and waiting time before service were considered to assess the flexibility of the Weighted Rayleigh distribution over existing distributions. The distributions performance were compared using Log-likelihood and Akaike Information Criteria (AIC). The Weighted Rayleigh distribution fits the real life data better than the Rayleigh, Inverse Weibull (IW) and Weighted Inverse Weibull (WIW) distributions.

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39

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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40

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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41

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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42

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/ejeng.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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43

Alzoubi, Loai, Ahmad Al-Khazaleh, Ayat Al-Meanazel, and Mohammed Gharaibeh. "EPANECHNIKOV-WEIBULL DISTRIBUTION." Journal of Southwest Jiaotong University 57, no. 6 (December 30, 2022): 949–58. http://dx.doi.org/10.35741/issn.0258-2724.57.6.81.

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The idea of using kernel functions combined with distributions to propose new distributions has recently been used to suggest new continuous distributions. This article combined the Epanechnikov kernel function with the Weibull distribution to produce the Epanechnikov-Weibull distribution (EWD). We have presented some properties of EWD, like the moments, MLEs, reliability analysis functions, Rényi entropy and the quantile function. We estimated the model parameters using the maximum likelihood method. A simulation study was conducted to calculate the MLE in terms of biases, mean square errors and mean relative, it shows that the estimates are consistent. Two real data set applications revealed that EWD is more flexible than the Weibull distribution.

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44

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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45

Ch, V. Sastry. "Marshall-Olkin Stereographic Circular Logistic Distribution." YMER Digital 21, no. 06 (June 22, 2022): 664–68. http://dx.doi.org/10.37896/ymer21.06/66.

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Marshall and Olkin (1997) proposed an interesting method of adding a new parameter to the existing distributions. The resulting distributions are called the MarshallOlkin distributions, these distributions include the original distributions as a special case and are more flexible and represent a wide range of behavior than the original distributions. In this paper, a new class of asymmetric stereographic circular logistic distribution is introduced by using Marshall-Olkin transformation on stereographic circular logistic distribution (Dattatreyarao et al (2016)), named as Marshall-Olkin Stereographic Circular Logistic Distribution. The proposed model admits closed form density and distribution functions, generalizes the stereographic circular logistic model and is more flexible to model various types of data (symmetric and skew-symmetric circular data). Keywords:Characteristics, Stereographic circular logistic distribution, circular data, Marshall-Olkin transformation, l -axial data.

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46

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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47

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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48

Velasquez, Edgar Daniel Rodriguez, and Nguyen Ngoc Thach. "Why Burr Distribution: Invariance-Based Explanation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 31, Supp02 (December 2023): 243–57. http://dx.doi.org/10.1142/s0218488523400147.

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In many practical situations, we observe a special Burr probability distribution — e.g., Burr distribution describes the distribution of people by income, it describes the rainfall data, etc. In this paper, we provide a theoretical explanation for the ubiquity of Burr distributions: namely, we show that this distribution naturally follows from scale-invariance. To be more precise, the simplest distribution that can be obtained from scale invariance is the Pareto distribution — a frequent particular case of the general Burr family of distributions. Next simplest are all distributions from the Burr family. We also use the scale-invariance approach to come up with a more general class of distributions that will, hopefully, provide an even more accurate description of the corresponding phenomena.

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49

MOALA, FERNANDO A., and SANKU DEY. "Objective and subjective prior distributions for the Gompertz distribution." Anais da Academia Brasileira de Ciências 90, no. 3 (September 2018): 2643–61. http://dx.doi.org/10.1590/0001-3765201820171040.

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50

Kumar, Dinesh, Sanjay Kumar Singh, and Umesh Singh. "Life Time Distributions: Derived from some Minimum Guarantee Distribution." Sohag Journal of Mathematics 4, no. 1 (January 1, 2017): 7–11. http://dx.doi.org/10.18576/sjm/040102.

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