Thematische Bibliographien / Exponential Family of distribution

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Auswahl der wissenschaftlichen Literatur zum Thema „Exponential Family of distribution“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Exponential Family of distribution"

Awodutire, Phillip. "Statistical Properties and Applications of the Exponentiated Chen-G Family of Distributions: Exponential Distribution as a Baseline Distribution." Austrian Journal of Statistics 51, no. 2 (2022): 57–90. http://dx.doi.org/10.17713/ajs.v51i2.1245.

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In this work, the Exponentiated Chen-G family of distributions is studied by generalizing the Chen-G family of distributions through the introduction of an additional shape parameter. The mixture properties of the derived family are studied. Some statistical properties of the family were considered, including moments, entropies, moment generating function, order statistics, quantile function. The estimation of the parameters of the family of distributions was done using the maximum likelihood estimation method, considering complete and censored situations. Using the Exponential distribution as

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Mahmoud, Mahmoud Riad, Moshera A. M. Ahmad, and AzzaE Ismail. "T-Inverse Exponential Family Of Distributions." Journal of University of Shanghai for Science and Technology 23, no. 09 (2021): 556–72. http://dx.doi.org/10.51201/jusst/21/08495.

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Recently, several methods have been introduced to generate neoteric distributions with more exibility, like T-X, T-R [Y] and alpha power. The T-Inverse exponential [Y] neoteric family of distributons is proposed in this paper utilising the T-R [Y] method. A generalised inverse exponential (IE) distribution family has been established. The distribution family is generated using quantile functions of some dierent distributions. A number of general features in the T-IE [Y] family are examined, like mean deviation, mode, moments, quantile function, and entropies. A special model of the T-IE [Y] di

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Hussein, Mohamed, Howaida Elsayed, and Gauss M. Cordeiro. "A New Family of Continuous Distributions: Properties and Estimation." Symmetry 14, no. 2 (2022): 276. http://dx.doi.org/10.3390/sym14020276.

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We introduce a new flexible modified alpha power (MAP) family of distributions by adding two parameters to a baseline model. Some of its mathematical properties are addressed. We show empirically that the new family is a good competitor to the Beta-F and Kumaraswamy-F classes, which have been widely applied in several areas. A new extension of the exponential distribution, called the modified alpha power exponential (MAPE) distribution, is defined by applying the MAP transformation to the exponential distribution. Some properties and maximum likelihood estimates are provided for this distribut

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Chinazom, Odom Conleth, Nduka Ethelbert Chinaka, and Ijomah Maxwell Azubuike. "The T-Exponentiated Exponential{Frechet} Family of Distributions: Theory and Applications." Asian Journal of Probability and Statistics 23, no. 4 (2023): 8–25. http://dx.doi.org/10.9734/ajpas/2023/v23i4509.

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This article introduces a new family of Generalized Exponentiated Exponential distribution. Using the T-R{Y} framework, a new family of T-Exponentiated Exponential{Y} distributions named T-Exponentiated Exponential{Frechet} family of distributions is proposed. Some general properties of the family such as hazard rate function, quantile function, non-central moment, mode, mean absolute deviations and Shannon’s entropy are discussed. A new continuous univariate probability distribution which is a member of the T-Exponentiated Exponential{Frechet} family of distributions is introduced. From the g

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Awodutire, Phillip Oluwatobi, Oluwafemi Samson Balogun, Akintayo Kehinde Olapade, and Ethelbert Chinaka Nduka. "The modified beta transmuted family of distributions with applications using the exponential distribution." PLOS ONE 16, no. 11 (2021): e0258512. http://dx.doi.org/10.1371/journal.pone.0258512.

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In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution. This derived family has the Beta Family of Distribution and the Transmuted family of distribution as subfamilies. The Modified beta transmuted frechet, modified beta transmuted exponential, modified beta transmuted gompertz and modified beta transmuted lindley were obtained as special cases. The analytical expressions were studied for some statistical properties of the derived family of distribution which includes the moments, moments

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Block, Henry W., Naftali A. Langberg, and Thomas H. Savits. "A MIXTURE OF EXPONENTIAL AND IFR GAMMA DISTRIBUTIONS HAVING AN UPSIDEDOWN BATHTUB-SHAPED FAILURE RATE." Probability in the Engineering and Informational Sciences 26, no. 4 (2012): 573–80. http://dx.doi.org/10.1017/s0269964812000204.

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We consider a mixture of one exponential distribution and one gamma distribution with increasing failure rate. For the right choice of parameters, it is shown that its failure rate has an upsidedown bathtub shape failure rate. We also consider a mixture of a family of exponentials and a family of gamma distributions and obtain a similar result.

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Makubate, Boikanyo, Broderick O. Oluyede, Gofaone Motobetso, Shujiao Huang, and Adeniyi F. Fagbamigbe. "The Beta Weibull-G Family of Distributions: Model, Properties and Application." International Journal of Statistics and Probability 7, no. 2 (2018): 12. http://dx.doi.org/10.5539/ijsp.v7n2p12.

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A new family of generalized distributions called the beta Weibull-G (BWG) distribution is proposed and developed. This new class of distributions has several new and well known distributions including exponentiated-G, Weibull-G, Rayleigh-G, exponential-G, beta exponential-G, beta Rayleigh-G, beta Rayleigh exponential, beta-exponential-exponential, Weibull-log-logistic distributions, as well as several other distributions such as beta Weibull-Uniform, beta Rayleigh-Uniform, beta exponential-Uniform, beta Weibull-log logistic and beta Weibull-exponential distributions as special cases. Series ex

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Reyes, Jimmy, Barry C. Arnold, Yolanda M. Gómez, Osvaldo Venegas, and Héctor W. Gómez. "Modified Bimodal Exponential Distribution with Applications." Axioms 14, no. 6 (2025): 461. https://doi.org/10.3390/axioms14060461.

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In this paper, we introduce a new distribution for modeling bimodal data supported on non-negative real numbers and particularly suited with an excess of very small values. This family of distributions is derived by multiplying the exponential distribution by a fourth-degree polynomial, resulting in a model that better fits the shape of the second mode of the empirical distribution of the data. We study the general density of this new family of distributions, along with its properties, moments, and skewness and kurtosis coefficients. A simulation study is performed to estimate parameters by th

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Abdullahi, J., S. U. Gulumbe, U. Usman, and A. I. Garba. "The Transform-Transformer Approach: Unveiling the Odd Transmuted Rayleigh-X Family of Distributions." International Journal of Science for Global Sustainability 9, no. 2 (2023): 85–98. http://dx.doi.org/10.57233/ijsgs.v9i2.462.

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The paper presents a novel class (family) of statistical distributions termed Odd Transmuted Rayleigh-X (OTR-X) that was created through a transform-transformer (T-X) approach. The CDF and PDF of the OTR-X family were derived. The available statistical literature studied earlier highlighted that almost all generalized distributions (in which one or more parameters were added) performed well and have better presentation of data than their counterparts with less number of parameters. This has motivated us to developed new family that is capable of producing new distributions. The research paper

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Badmus, N. I., Olanrewaju Faweya, and K. A. Adeleke. "Generalized Beta-Exponential Weibull Distribution and its Applications." Journal of Statistics: Advances in Theory and Applications 24, no. 1 (2020): 1–33. http://dx.doi.org/10.18642/jsata_7100122158.

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In this article, we investigate a distribution called the generalized beta-exponential Weibull distribution. Beta exponential x family of link function which is generated from family of generalized distributions is used in generating the new distribution. Its density and hazard functions have different shapes and contains special case of distributions that have been proposed in literature such as beta-Weibull, beta exponential, exponentiated-Weibull and exponentiated-exponential distribution. Various properties of the distribution were obtained namely; moments, generating function, Renyi entro

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Dissertationen zum Thema "Exponential Family of distribution"

Lai, Yanzhao. "Generalized method of moments exponential distribution family." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/laiy/yanzhaolai.pdf.

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Hornik, Kurt, and Bettina Grün. "On standard conjugate families for natural exponential families with bounded natural parameter space." Elsevier, 2014. http://dx.doi.org/10.1016/j.jmva.2014.01.003.

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Diaconis and Ylvisaker (1979) give necessary conditions for conjugate priors for distributions from the natural exponential family to be proper as well as to have the property of linear posterior expectation of the mean parameter of the family. Their conditions for propriety and linear posterior expectation are also sufficient if the natural parameter space is equal to the set of all d-dimensional real numbers. In this paper their results are extended to characterize when conjugate priors are proper if the natural parameter space is bounded. For the special case where the natural exponential

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Wang, Zhizheng. "Hardware Utilization Measurement and Optimization: A Statistical Investigation and Simulation Study." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260070.

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It is essential for the managers to make investment on hardware based on the utilization information of the equipment. From December 2014, a pool of hardware and a scheduling and resource sharing system is implemented by one of the software testing sections in Ericsson. To monitor the efficiency of these equipment and the workflow, a model of non-homogeneous M/M/c queue is developed that successfully captures the main aspects of the system. The model is decomposed into arrival, service, failure and each part is estimated. Mixture exponential is estimated with EM algorithm and the impact of sch

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Ruddy, Sean Matthew. "Shrinkage of dispersion parameters in the double exponential family of distributions, with applications to genomic sequencing." Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3686002.

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<p> The prevalence of sequencing experiments in genomics has led to an increased use of methods for count data in analyzing high-throughput genomic data to perform analyses. The importance of shrinkage methods in improving the performance of statistical methods remains. A common example is that of gene expression data, where the counts per gene are often modeled as some form of an overdispersed Poisson. In this case, shrinkage estimates of the per-gene dispersion parameter have lead to improved estimation of dispersion in the case of a small number of samples. We address a different count sett

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Ibukun, Michael Abimbola. "Modely s Touchardovým rozdělením." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445468.

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In 2018, Raul Matsushita, Donald Pianto, Bernardo B. De Andrade, Andre Cançado & Sergio Da Silva published a paper titled ”Touchard distribution”, which presented a model that is a two-parameter extension of the Poisson distribution. This model has its normalizing constant related to the Touchard polynomials, hence the name of this model. This diploma thesis is concerned with the properties of the Touchard distribution for which delta is known. Two asymptotic tests based on two different statistics were carried out for comparison in a Touchard model with two independent samples, supported by s

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Okada, Daigo. "Decomposition of a set of distributions in extended exponential family form for distinguishing multiple oligo-dimensional marker expression profiles of single-cell populations and visualizing their dynamics." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263569.

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Sears, Timothy Dean, and tim sears@biogreenoil com. "Generalized Maximum Entropy, Convexity and Machine Learning." The Australian National University. Research School of Information Sciences and Engineering, 2008. http://thesis.anu.edu.au./public/adt-ANU20090525.210315.

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This thesis identiﬁes and extends techniques that can be linked to the principle of maximum entropy (maxent) and applied to parameter estimation in machine learning and statistics. Entropy functions based on deformed logarithms are used to construct Bregman divergences, and together these represent a generalization of relative entropy. The framework is analyzed using convex analysis to charac- terize generalized forms of exponential family distributions. Various connections to the existing machine learning literature are discussed and the techniques are applied to the problem of non-neg

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TOZZO, VERONICA. "Generalised temporal network inference." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/986950.

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Network inference is becoming increasingly central in the analysis of complex phenomena as it allows to obtain understandable models of entities interactions. Among the many possible graphical models, Markov Random Fields are widely used as they are strictly connected to a probability distribution assumption that allow to model a variety of different data. The inference of such models can be guided by two priors: sparsity and non-stationarity. In other words, only few connections are necessary to explain the phenomenon under observation and, as the phenomenon evolves, the underlying connection

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Silva, Michel Ferreira da. "Estimação e teste de hipótese baseados em verossimilhanças perfiladas." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-06122006-162733/.

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Tratar a função de verossimilhança perfilada como uma verossimilhança genuína pode levar a alguns problemas, como, por exemplo, inconsistência e ineficiência dos estimadores de máxima verossimilhança. Outro problema comum refere-se à aproximação usual da distribuição da estatística da razão de verossimilhanças pela distribuição qui-quadrado, que, dependendo da quantidade de parâmetros de perturbação, pode ser muito pobre. Desta forma, torna-se importante obter ajustes para tal função. Vários pesquisadores, incluindo Barndorff-Nielsen (1983,1994), Cox e Reid (1987,1992), McCullagh e Tib

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Gutierrez-Pena, Eduardo Arturo. "Bayesian topics relating to the exponential family." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/8062.

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Bücher zum Thema "Exponential Family of distribution"

Lye, Jenny N. Approximating distributions using the generalized exponential family. Dept. of Economics, University of Melbourne, 1991.

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Martin, Vance L. A generalized parametric exponential family approach to modelling the distribution of exchange rate movements. Dept. of Economics, University of Melbourne, 1991.

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Hamedani, G. G. (Gholamhossein G.), ed. Exponential distribution: Theory and methods. Nova Science Publishers, 2009.

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Rudolph, Maja. Exponential Family Embeddings. [publisher not identified], 2018.

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Azlarov, T. A., and N. A. Volodin. Characterization Problems Associated with the Exponential Distribution. Edited by Ingram Olkin. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4956-6.

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1956-, Balakrishnan N., and Basu Asit P, eds. The exponential distribution: Theory, methods, and applications. Gordon and Breach, 1995.

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Seshadri, V. The inverse Gaussian distribution: A case study in exponential families. Clarendon Press, 1993.

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Chun, Jin, and Lim Wooi K, eds. Handbook of exponential and related distributions for engineers and scientists. Chapman & Hall/CRC, 2005.

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Pal, Nabendu. Handbook of exponential and related distributions for engineers and scientists. Chapman & Hall/CRC, 2006.

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Jasso, Guillermina. A new continuous distribution and two new families of distributions based on the exponential. IZA, 2007.

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Buchteile zum Thema "Exponential Family of distribution"

Wüthrich, Mario V., and Michael Merz. "Exponential Dispersion Family." In Springer Actuarial. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_2.

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AbstractThis chapter introduces and discusses the exponential family (EF) and the exponential dispersion family (EDF). The EF and the EDF are by far the most important classes of distribution functions for regression modeling. They include, among others, the Gaussian, the binomial, the Poisson, the gamma, the inverse Gaussian distributions, as well as Tweedie’s models. We introduce these families of distribution functions, discuss their properties and provide several examples. Moreover, we introduce the Kullback–Leibler (KL) divergence and the Bregman divergence, which are important tools in m

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AL-Hussaini, Essam K., and Mohammad Ahsanullah. "Family of Exponentiated Exponential Distribution." In Atlantis Studies in Probability and Statistics. Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-079-9_4.

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Islam, M. Ataharul, and Rafiqul I. Chowdhury. "Exponential Family of Distributions." In Analysis of Repeated Measures Data. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3794-8_3.

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Islam, M. Ataharul, and Soma Chowdhury Biswas. "Exponential Family of Distributions." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_2.

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Haberman, Shelby J. "Exponential Family Distributions Relevant to IRT." In Handbook of Item Response Theory. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b19166-4.

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He, Xiaodong, and Li Deng. "Discriminative Learning Algorithm for Exponential-Family Distributions." In Discriminative Learning for Speech Recognition. Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-02557-0_4.

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Dobson, Annette J. "Exponential family of distributions and generalized linear models." In An Introduction to Generalized Linear Models. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_3.

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Lin, Yu-Jau, Tzong-Ru Tsai, Ding-Geng Chen, and Yuhlong Lio. "A Competing Risk Model Based on a Two-Parameter Exponential Family Distribution Under Progressive Type II Censoring." In Emerging Topics in Statistics and Biostatistics. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-88658-5_4.

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Zamzami, Nuha, and Nizar Bouguila. "Deriving Probabilistic SVM Kernels from Exponential Family Approximations to Multivariate Distributions for Count Data." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23876-6_7.

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Salinas Ruíz, Josafhat, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, and Jose Crossa Hiriart. "Generalized Linear Mixed Models for Non-normal Responses." In Generalized Linear Mixed Models with Applications in Agriculture and Biology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32800-8_4.

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AbstractGeneralized linear mixed models (GLMMs) have been recognized as one of the major methodological developments in recent years, which is evidenced by the increased use of such sophisticated statistical tools with broader applicability and flexibility. This family of models can be applied to a wide range of different data types (continuous, categorical (nominal or ordinal), percentages, and counts), and each is appropriate for a specific type of data. This modern methodology allows data to be described through a distribution of the exponential family that best fits the response variable.

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Konferenzberichte zum Thema "Exponential Family of distribution"

Ogunwale, Olukunle Daniel, Kehinde Peter Ajewole, Korede Peter Olayinka, Ezekiel Olaoluwa Omole, Femi Emmanuel Amoyedo, and Oluwadamilare J. Akinremi. "A New Family of Continuous Probability Distribution: Gamma-Exponential Distribution (GED)Theory and its Properties." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10630048.

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Mukherjee, Arpan, and Ali Tajer. "BAI in Exponential Family: Efficiency and Optimality." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619690.

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Ajewole, Kehinde Peter, Olukunle Daniel Ogunwale, Fatai Alani Osunronbi, Peter Onu, Ezekiel Olaoluwa Omole, and Femi Emmanuel Amoyedo. "Exponential-Exponential-Weibull Distribution (EEWD) Modeling: Theory, Characteristics, and Use on Actual Data." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10630231.

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Daum, Frederick E., Liyi Dai, Jim Huang, and Arjang Noushin. "Bayesian deep learning with particle flow using the exponential family of probability densities." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2025. https://doi.org/10.1117/12.3050158.

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Mohsin, Layla Abdul Jaleel, and Hazim Ghdhaib Kalt. "An application to exponential distribution of a novel family of distributions." In 6TH INTERNATIONAL CONFERENCE FOR PHYSICS AND ADVANCE COMPUTATION SCIENCES: ICPAS2024. AIP Publishing, 2025. https://doi.org/10.1063/5.0264854.

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Malagò, Luigi, Matteo Matteucci, and Giovanni Pistone. "Towards the geometry of estimation of distribution algorithms based on the exponential family." In the 11th workshop proceedings. ACM Press, 2011. http://dx.doi.org/10.1145/1967654.1967675.

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Elkan, Charles. "Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution." In the 23rd international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143881.

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Cheung, V. C. K., and M. C. Tresch. "Non-negative matrix factorization algorithms modeling noise distributions within the exponential family." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615595.

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Hamad, Doaa Khairi, and Feras Sh M. Batah. "An Investigation into the Exponential T-X Family Distributions to Simulation study." In AICCONF '24: Cognitive Models and Artificial Intelligence Conference. ACM, 2024. http://dx.doi.org/10.1145/3660853.3660897.

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Saleh, Hiba Mahdi, and Ali Talib Mohammed. "Alpha power transformation family distributions: Properties and application to the exponential Weibull model." In THE INTERNATIONAL SCIENTIFIC CONFERENCE OF ENGINEERING SCIENCES AND ADVANCED TECHNOLOGIES. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0236939.

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Berichte der Organisationen zum Thema "Exponential Family of distribution"

Gupta, Shanti S., and Jianjun Li. Empirical Bayes Tests For Some Non-Exponential Distribution Family. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada370172.

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Liang, TaChen. On a Sequential Subset Selection Procedure for Exponential Family Distributions. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200013.

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Kay, Steven, Haibo He, and Quan Ding. The Exponentially Embedded Family of Distributions for Effective Data Representation, Information Extraction, and Decision Making. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada582481.

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Gupta, Shanti S., and Jianjun Li. On Empirical Bayes Procedures for Selecting Good Populations in Positive Exponential Family. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada395254.

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Gupta, Shanti S., and Friedrich Liese. Asymptotic Distribution of the Random Regret Risk for Selecting Exponential Populations. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada358189.

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Kaplow, Louis. Optimal Distribution and Taxation of the Family. National Bureau of Economic Research, 1992. http://dx.doi.org/10.3386/w4189.

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Dube, Arindrajit. Minimum Wages and the Distribution of Family Incomes. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w25240.

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Altonji, Joseph, Disa Hynsjö, and Ivan Vidangos. Individual Earnings and Family Income: Dynamics and Distribution. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30095.

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Phillips, James, Wendy Greene, and Elizabeth Jackson. Lessons from community-based distribution of family planning in Africa. Population Council, 1999. http://dx.doi.org/10.31899/pgy6.1022.

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Maggwa, Baker, Ian Askew, Caroline Marangwanda, Ronika Nyakauru, and Barbara Janowitz. An assessment of the Zimbabwe National Family Planning Council's community based distribution programme. Population Council, 2001. http://dx.doi.org/10.31899/rh4.1225.

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