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Relevant bibliographies by topics / Exponential Family of distribution

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Academic literature on the topic 'Exponential Family of distribution'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Exponential Family of distribution"

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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 (September 13, 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] distribution family was one of those old distributions. Certain distribution examples are produced by the T-IE [Y] family. An applied case was presented which showed the importance of the neoteric family.

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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 (January 18, 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 expansion of the density function, hazard function, moments, mean deviations, Lorenz and Bonferroni curves, R\'enyi entropy, distribution of order statistics and maximum likelihood estimates of the model parameters are given. Application of the model to real data set is presented to illustrate the importance and usefulness of the special case beta Weibull-log-logistic distribution.

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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 (July 30, 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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Louzada, Francisco, Vitor Marchi, and James Carpenter. "The Complementary Exponentiated Exponential Geometric Lifetime Distribution." Journal of Probability and Statistics 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/502159.

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We proposed a new family of lifetime distributions, namely, complementary exponentiated exponential geometric distribution. This new family arises on a latent competing risk scenario, where the lifetime associated with a particular risk is not observable but only the maximum lifetime value among all risks. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulas for its survival and hazard functions, moments,rth moment of theith order statistic, mean residual lifetime, and modal value. Inference is implemented via a straightforwardly maximum likelihood procedure. The practical importance of the new distribution was demonstrated in three applications where our distribution outperforms several former lifetime distributions, such as the exponential, the exponential-geometric, the Weibull, the modified Weibull, and the generalized exponential-Poisson distribution.

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Ghorbanpour, Samereh, Rahim Chinipardaz, and Seyed Mohammad Reza Alavi. "Form-Invariance of the Non-Regular Exponential Family of Distributions." Revista Colombiana de Estadística 41, no. 2 (July 1, 2018): 157–72. http://dx.doi.org/10.15446/rce.v41n2.62233.

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The weighted distributions are used when the sampling mechanism records observations according to a nonnegative weight function. Sometimes the form of the weighted distribution is the same as the original distribution except possibly for a change in the parameters that is called the form-invariant weighted distribution. In this paper, by identifying a general class of weight functions, we introduce an extended class of form-invariant weighted distributions belonging to the non-regular exponential family which included two common families of distribution: exponential family and non-regular family as special cases. Some properties of this class of distributions such as the sufficient and minimal sufficient statistics, maximum likelihood estimation and the Fisher information matrix are studied.

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Iwasaki, Masakazu, and Hiroe Tsubaki. "A new bivariate distribution in natural exponential family." Metrika 61, no. 3 (June 2005): 323–36. http://dx.doi.org/10.1007/s001840400348.

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Abdulkadir, Dr Sauta Saidu, J. Jerry, and T. G. Ieren. "STATISTICAL PROPERTIES OF LOMAX-INVERSE EXPONENTIAL DISTRIBUTION AND APPLICATIONS TO REAL LIFE DATA." FUDMA JOURNAL OF SCIENCES 4, no. 2 (October 7, 2020): 680–94. http://dx.doi.org/10.33003/fjs-2020-0402-435.

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This paper proposes a Lomax-inverse exponential distribution as an improvement on the inverse exponential distribution in the form of Lomax-inverse Exponential using the Lomax generator (Lomax-G family) with two extra parameters to generalize any continuous distribution (CDF). The probability density function (PDF) and cumulative distribution function (CDF) of the Lomax-inverse exponential distribution are defined. Some basic properties of the new distribution are derived and extensively studied. The unknown parameters estimation of the distribution is done by method of maximum likelihood estimation. Three real-life datasets are used to assess the performance of the proposed probability distribution in comparison with some other generalizations of Lomax distribution. It is observed that Lomax-inverse exponential distribution is more robust than the competing distributions, inverse exponential and Lomax distributions. This is an evident that the Lomax generator is a good probability model.

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Zubair, Muhammad, Ayman Alzaatreh, Gauss Cordeiro, M. H. Tahir, and Muhammad Mansoor. "On generalized classes of exponential distribution using T-X family framework." Filomat 32, no. 4 (2018): 1259–72. http://dx.doi.org/10.2298/fil1804259z.

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We introduce new generalized classes of exponential distribution, called T-exponential {Y} class using the quantile functions of well-known distributions. We derive some general mathematical properties of this class including explicit expressions for the quantile function, Shannon entropy, moments and mean deviations. Some generalized exponential families are investigated. The shapes of the models in these families can be symmetric, left-skewed, right-skewed and reversed-J, and the hazard rate can be increasing, decreasing, bathtub, upside-down bathtub, J and reverse-J shaped. Two real data sets are used to illustrate the applicability of the new models.

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Abouelmagd, T. H. M. "The Logarithmic Burr-Hatke Exponential Distribution for Modeling Reliability and Medical Data." International Journal of Statistics and Probability 7, no. 5 (August 9, 2018): 73. http://dx.doi.org/10.5539/ijsp.v7n5p73.

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In this work, we introduced a new one-parameter exponential distribution. Some of its structural properties are derived% \textbf{.} The maximum likelihood method is used to estimate the model parameters by means of numerical Monte Carlo simulation study. The justification for the practicality of the new lifetime model is based on the wider use of the exponential model. The new model can be viewed as a mixtureof the exponentiated exponential distribution. It can also be considered as a suitable model for fitting right skewed data.\textbf{\ }We prove empirically the importance and flexibility of the new model in modelingcancer patients data, the new model provides adequate fits as compared to other related models with small values for $W^{\ast }$\ \ and $A^{\ast }$. The new model is much better than the Modified beta-Weibull, Weibull, exponentiated transmuted generalized Rayleig, the transmuted modified-Weibull, and transmuted additive Weibull models in modeling cancer patients data. We are also motivated to introduce this new model because it has only one parameter and we can generate some new families based on it such as the the odd Burr-Hatke exponential-G family of distributions, the logarithmic\textbf{\ }Burr-Hatke exponential-G family of distributions and the generalized\textbf{\ }Burr-Hatke exponential-G family of distributions, among others.

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Bilal, Muhammad, Muhammad Mohsin, and Muhammad Aslam. "Weibull-Exponential Distribution and Its Application in Monitoring Industrial Process." Mathematical Problems in Engineering 2021 (March 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/6650237.

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This paper presents a new Weibull family of distributions. The compatibility of the newly developed class is justified through its application in the field of quality control using Weibull-exponential distribution, a special case of the proposed family. In this paper, an attribute control chart using Weibull-exponential distribution is developed. The estimations of the model parameters and the proposed chart parameters are performed through the methods of maximum likelihood and average run-length. The significance of the proposed model is demonstrated using a simulation study and real-life problems. The results of the monitoring process and quick detection are compared with exponential distribution.

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Dissertations / Theses on the topic "Exponential Family of distribution"

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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 family is through a spherical probability distribution n,we show that the proper conjugate priors can be characterized by the behavior of the moment generating function of n at the boundary of the natural parameter space, or the second-order tail behavior of n. In addition, we show that if these families are non-regular, then linear posterior expectation never holds. The results for this special case are also extended to natural exponential families through elliptical probability distributions.

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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 scheduling change is also examined. Finally a simulation of workflow is done with Python module and the optimized number of hardware is proposed based on this M/M/c queue system.

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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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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 setting introduced by the use of sequencing data: comparing differential proportional usage via an overdispersed binomial model. Such a model can be useful for testing differential exon inclusion in mRNA-Seq experiments in addition to the typical differential gene expression analysis. In this setting, there are fewer such shrinkage methods for the dispersion parameter. We introduce a novel method that is developed by modeling the dispersion based on the double exponential family of distributions proposed by Efron (1986), also known as the exponential dispersion model (Jorgensen, 1987). Our methods (WEB-Seq and DEB-Seq) are empirical bayes strategies for producing a shrunken estimate of dispersion that can be applied to any double exponential dispersion family, though we focus on the binomial and poisson. These methods effectively detect differential proportional usage, and have close ties to the weighted likelihood strategy of edgeR developed for gene expression data (Robinson and Smyth, 2007; Robinson et al., 2010). We analyze their behavior on simulated data sets as well as real data for both differential exon usage and differential gene expression. In the exon usage case, we will demonstrate our methods' superior ability to control the FDR and detect truly different features compared to existing methods. In the gene expression setting, our methods fail to control the FDR; however, the rankings of the genes by p-value is among the top performers and proves to be robust to both changes in the probability distribution used to generate the counts and in low sample size situations. We provide implementation of our methods in the R package DoubleExpSeq available from the Comprehensive R Archive Network (CRAN).

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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 simulations in R.

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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-negative matrix factorization (NMF).

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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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Kosmidis, Ioannis. "Bias reduction in exponential family nonlinear models." Thesis, University of Warwick, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492241.

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The modified-score functions approach to bias reduction (Firth, 1993) is continually gaining in popularity (e.g. Mehrabi & Matthews, 1995; Pettitt et al., 1998; Heinze & Schemper, 2002; Bull et al., 2002; Zorn, 2005; Sartori, 2006; Bull et al., 2007), because of the superior properties of the bias-reduced estimator over the traditional maximum likelihood estimator, particularly in models for categorical responses. Most of the activity is noted for logistic regression, where the bias-reduction method neatly corresponds to penalization of the likelihood by Jeffreys prior and the bias-reduced estimates are always finite and beneficially shrink towards the origin. The recent applied and methodological interest in the bias-reduction method motivates the current thesis and the aim is to explore the nature and widen the applicability of the method, identifying cases where bias reduction is beneficial. Particularly, the current thesis focuses on the following three targets: i) To explore the nature of the bias-reducing modifications to the efficient scores and to obtain results that facilitate the application and the theoretical assessment of the bias-reduction method. ii) To establish theoretically that the bias-reduction method should be considered as an improvement over traditional ML for logistic regressions. iii) To deviate from the flat exponential family and explore the effect of bias reduction in some commonly used curved models for categorical responses.

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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 Tibshirani (1990) e Stern (1997), propuseram modificações à função de verossimilhança perfilada. Tais ajustes consistem na incorporação de um termo à verossimilhança perfilada anteriormente à estimação e têm o efeito de diminuir os vieses da função escore e da informação. Este trabalho faz uma revisão desses ajustes e das aproximações para o ajuste de Barndorff-Nielsen (1983,1994) descritas em Severini (2000a). São apresentadas suas derivações, bem como suas propriedades. Para ilustrar suas aplicações, são derivados tais ajustes no contexto da família exponencial biparamétrica. Resultados de simulações de Monte Carlo são apresentados a fim de avaliar os desempenhos dos estimadores de máxima verossimilhança e dos testes da razão de verossimilhanças baseados em tais funções. Também são apresentadas aplicações dessas funções de verossimilhança em modelos não pertencentes à família exponencial biparamétrica, mais precisamente, na família de distribuições GA0(alfa,gama,L), usada para modelar dados de imagens de radar, e no modelo de Weibull, muito usado em aplicações da área da engenharia denominada confiabilidade, considerando dados completos e censurados. Aqui também foram obtidos resultados numéricos a fim de avaliar a qualidade dos ajustes sobre a verossimilhança perfilada, analogamente às simulações realizadas para a família exponencial biparamétrica. Vale mencionar que, no caso da família de distribuições GA0(alfa,gama,L), foi avaliada a aproximação da distribuição da estatística da razão de verossimilhanças sinalizada pela distribuição normal padrão. Além disso, no caso do modelo de Weibull, vale destacar que foram derivados resultados distribucionais relativos aos estimadores de máxima verossimilhança e às estatísticas da razão de verossimilhanças para dados completos e censurados, apresentados em apêndice.
The profile likelihood function is not genuine likelihood function, and profile maximum likelihood estimators are typically inefficient and inconsistent. Additionally, the null distribution of the likelihood ratio test statistic can be poorly approximated by the asymptotic chi-squared distribution in finite samples when there are nuisance parameters. It is thus important to obtain adjustments to the likelihood function. Several authors, including Barndorff-Nielsen (1983,1994), Cox and Reid (1987,1992), McCullagh and Tibshirani (1990) and Stern (1997), have proposed modifications to the profile likelihood function. They are defined in a such a way to reduce the score and information biases. In this dissertation, we review several profile likelihood adjustments and also approximations to the adjustments proposed by Barndorff-Nielsen (1983,1994), also described in Severini (2000a). We present derivations and the main properties of the different adjustments. We also obtain adjustments for likelihood-based inference in the two-parameter exponential family. Numerical results on estimation and testing are provided. We also consider models that do not belong to the two-parameter exponential family: the GA0(alfa,gama,L) family, which is commonly used to model image radar data, and the Weibull model, which is useful for reliability studies, the latter under both noncensored and censored data. Again, extensive numerical results are provided. It is noteworthy that, in the context of the GA0(alfa,gama,L) model, we have evaluated the approximation of the null distribution of the signalized likelihood ratio statistic by the standard normal distribution. Additionally, we have obtained distributional results for the Weibull case concerning the maximum likelihood estimators and the likelihood ratio statistic both for noncensored and censored data.

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Books on the topic "Exponential Family of distribution"

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

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

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Exponential family nonlinear models. Singapore: Springer, 1998.

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Characterization problems associated with the exponential distribution. New York: Springer-Verlag, 1986.

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

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

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

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

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

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Book chapters on the topic "Exponential Family of distribution"

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AL-Hussaini, Essam K., and Mohammad Ahsanullah. "Family of Exponentiated Exponential Distribution." In Atlantis Studies in Probability and Statistics, 81–102. Paris: 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, 23–30. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3794-8_3.

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Haberman, Shelby J. "Exponential Family Distributions Relevant to IRT." In Handbook of Item Response Theory, 47–70. Boca Raton, FL: CRC Press, 2015- | Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b19166-4.

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Dobson, Annette J. "Exponential family of distributions and generalized linear models." In An Introduction to Generalized Linear Models, 26–35. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_3.

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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, 125–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23876-6_7.

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Chen, (Din) Ding-Geng, and Yuhlong Lio. "A Family of Generalized Rayleigh-Exponential-Weibull Distribution and Its Application to Modeling the Progressively Type-I Interval Censored Data." In Emerging Topics in Statistics and Biostatistics, 529–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42196-0_23.

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Gupta, Arjun K., Wei-Bin Zeng, and Yanhong Wu. "Exponential Distribution." In Probability and Statistical Models, 23–43. Boston, MA: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4987-6_2.

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Singh, Vijay P. "Exponential Distribution." In Entropy-Based Parameter Estimation in Hydrology, 49–55. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-1431-0_4.

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Sundberg, Rolf. "Exponential Family Models." In International Encyclopedia of Statistical Science, 490–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_243.

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Zerom, Dawit, and Zvi Drezner. "A Bivariate Exponential Distribution." In Contributions to Location Analysis, 343–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19111-5_14.

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Conference papers on the topic "Exponential Family of distribution"

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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. New York, New York, USA: 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. New York, New York, USA: 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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Abernethy, Jacob, Sindhu Kutty, Sébastien Lahaie, and Rahul Sami. "Information aggregation in exponential family markets." In EC '14: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600057.2602896.

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Sabeti, Elyas, and Anders Host-Madsen. "Atypicality for the class of exponential family." In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2016. http://dx.doi.org/10.1109/allerton.2016.7852292.

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6

Shi, Zheyuan, and Sindhu Kutty. "Strategic reporting in exponential family prediction markets." In 2016 IEEE MIT Undergraduate Research Technology Conference (URTC). IEEE, 2016. http://dx.doi.org/10.1109/urtc.2016.8284063.

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Abbasnejad, Iman, M. Javad Zomorodian, M. Amin Abbasnejad, and Hossein Ajdari. "Pose recognition using mixture of exponential family." In 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2012. http://dx.doi.org/10.1109/aisp.2012.6313760.

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Rish, Irina, and Genady Grabarnik. "Sparse signal recovery with exponential-family noise." In 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2009. http://dx.doi.org/10.1109/allerton.2009.5394837.

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Pratama, B. N., S. Nurrohmah, and I. Fithriani. "Composite Exponential-Pareto distribution." In PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059049.

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Varshney, Lav R. "Two way communication over exponential family type channels." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620735.

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Reports on the topic "Exponential Family of distribution"

1

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

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2

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

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3

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

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4

Gupta, Shanti S., and Jianjun Li. On Empirical Bayes Procedures for Selecting Good Populations in Positive Exponential Family. Fort Belvoir, VA: Defense Technical Information Center, August 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. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada358189.

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

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

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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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Bartik, Timothy J. The Effects of Metropolitan Job Growth on the Size Distribution of Family Income. W.E. Upjohn Institute, March 1991. http://dx.doi.org/10.17848/wp91-06.

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