Relevant bibliographies by topics / Mixture of Kumaraswamy distribution

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mixture of Kumaraswamy distribution'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Mixture of Kumaraswamy distribution.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Mixture of Kumaraswamy distribution"

1

Rocha, Ricardo, Saralees Nadarajah, Vera Tomazella, Francisco Louzada, and Amanda Eudes. "New defective models based on the Kumaraswamy family of distributions with application to cancer data sets." Statistical Methods in Medical Research 26, no. 4 (June 19, 2015): 1737–55. http://dx.doi.org/10.1177/0962280215587976.

Full text
Abstract:
An alternative to the standard mixture model is proposed for modeling data containing cured elements or a cure fraction. This approach is based on the use of defective distributions to estimate the cure fraction as a function of the estimated parameters. In the literature there are just two of these distributions: the Gompertz and the inverse Gaussian. Here, we propose two new defective distributions: the Kumaraswamy Gompertz and Kumaraswamy inverse Gaussian distributions, extensions of the Gompertz and inverse Gaussian distributions under the Kumaraswamy family of distributions. We show in fact that if a distribution is defective, then its extension under the Kumaraswamy family is defective too. We consider maximum likelihood estimation of the extensions and check its finite sample performance. We use three real cancer data sets to show that the new defective distributions offer better fits than baseline distributions.
APA, Harvard, Vancouver, ISO, and other styles
2

Ghosh, Indranil. "A NEW CLASS OF KUMARASWAMY MIXTURE DISTRIBUTION FOR INCOME MODELING." Far East Journal of Theoretical Statistics 51, no. 3 (February 11, 2016): 129–51. http://dx.doi.org/10.17654/fjtsnov2015_129_151.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Noor, Farzana, Saadia Masood, Mehwish Zaman, Maryam Siddiqa, Raja Asif Wagan, Imran Ullah Khan, and Ahthasham Sajid. "Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals." Mathematical Problems in Engineering 2021 (May 18, 2021): 1–18. http://dx.doi.org/10.1155/2021/5569652.

Full text
Abstract:
Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.
APA, Harvard, Vancouver, ISO, and other styles
4

ZeinEldin, Ramadan A., Farrukh Jamal, Christophe Chesneau, and Mohammed Elgarhy. "Type II Topp–Leone Inverted Kumaraswamy Distribution with Statistical Inference and Applications." Symmetry 11, no. 12 (November 28, 2019): 1459. http://dx.doi.org/10.3390/sym11121459.

Full text
Abstract:
In this paper, we present and study a new four-parameter lifetime distribution obtained by the combination of the so-called type II Topp–Leone-G and transmuted-G families and the inverted Kumaraswamy distribution. By construction, the new distribution enjoys nice flexible properties and covers some well-known distributions which have already proven themselves in statistical applications, including some extensions of the Bur XII distribution. We first present the main functions related to the new distribution, with discussions on their shapes. In particular, we show that the related probability density function is left, right skewed, near symmetrical and reverse J shaped, with a notable difference regarding the right tailed, illustrating the flexibility of the distribution. Then, the related model is displayed, with the estimation of the parameters by the maximum likelihood method and the consideration of two practical data sets. We show that the proposed model is the best one in terms of standard model selection criteria, including Akaike information and Bayesian information criteria, and goodness of fit tests against three well-established competitors. Then, for the new model, the theoretical background on the maximum likelihood method is given, with numerical guaranties of the efficiency of the estimates obtained via a simulation study. Finally, the main mathematical properties of the new distribution are discussed, including asymptotic results, quantile function, Bowley skewness and Moors kurtosis, mixture representations for the probability density and cumulative density functions, ordinary moments, incomplete moments, probability weighted moments, stress-strength reliability and order statistics.
APA, Harvard, Vancouver, ISO, and other styles
5

Adham, Samia A., and Anfal A. ALgfary. "Bayesian estimation and prediction for a mixture of exponentiated Kumaraswamy distributions." International Journal of Contemporary Mathematical Sciences 11 (2016): 497–508. http://dx.doi.org/10.12988/ijcms.2016.61165.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lawal, Bayo H. "On Some Mixture Models for Over-dispersed Binary Data." International Journal of Statistics and Probability 6, no. 2 (February 27, 2017): 134. http://dx.doi.org/10.5539/ijsp.v6n2p134.

Full text
Abstract:
In this paper, we consider several binomial mixture models for fitting over-dispersed binary data. The models range from the binomial itself, to the beta-binomial (BB), the Kumaraswamy distributions I and II (KPI \& KPII) as well as the McDonald generalized beta-binomial mixed model (McGBB). The models are applied to five data sets that have received attention in various literature. Because of convergence issues, several optimization methods ranging from the Newton-Raphson to the quasi-Newton optimization algorithms were employed with SAS PROC NLMIXED using the Adaptive Gaussian Quadrature as the integral approximation method within PROC NLMIXED. Our results differ from those presented in Li, Huang and Zhao (2011) for the example data sets in that paper but agree with those presented in Manoj, Wijekoon and Yapa (2013). We also applied these models to the case where we have a $k$ vector of covariates $(x_1, x_2, \ldots, x_k)^{'}$. Our results here suggest that the McGBB performs better than the other models in the GLM framework. All computations in this paper employed PROC NLMIXED in SAS. We present in the appendix a sample of the SAS program employed for implementing the McGBB model for one of the examples.
APA, Harvard, Vancouver, ISO, and other styles
7

Shuaib Khan, Muhammad, Robert King, and Irene Lena Hudson. "TRANSMUTED KUMARASWAMY DISTRIBUTION." Statistics in Transition. New Series 17, no. 2 (2016): 183–210. http://dx.doi.org/10.21307/stattrans-2016-013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ahmed, Mohamed Ali, Mahmoud Riad Mahmoud, and Elsayed Ahmed ElSherpieny. "The New Kumaraswamy Kumaraswamy Weibull Distribution with Application." Pakistan Journal of Statistics and Operation Research 12, no. 1 (March 2, 2016): 165. http://dx.doi.org/10.18187/pjsor.v12i1.1129.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Nassar, Manal Mohamed. "The Kumaraswamy Laplace Distribution." Pakistan Journal of Statistics and Operation Research 12, no. 4 (December 1, 2016): 609. http://dx.doi.org/10.18187/pjsor.v12i4.1485.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bourguignon, Marcelo, Rodrigo B. Silva, Luz M. Zea, and Gauss M. Cordeiro. "The Kumaraswamy Pareto distribution." Journal of Statistical Theory and Applications 12, no. 2 (2013): 129. http://dx.doi.org/10.2991/jsta.2013.12.2.1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Mixture of Kumaraswamy distribution"

1

Basalamah, Doaa. "Statistical Inference for a New Class of Skew t Distribution and Its Related Properties." Bowling Green State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1496762068499547.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lima, Stênio Rodrigues. "The half-normal generalized family and Kumaraswamy Nadarajah-Haghighi distribution." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/14917.

Full text
Abstract:
Submitted by Isaac Francisco de Souza Dias (isaac.souzadias@ufpe.br) on 2016-01-15T19:29:39Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao-Stênio.pdf: 814092 bytes, checksum: b35ab296b97a20fb9607af8834520116 (MD5)
Made available in DSpace on 2016-01-15T19:29:39Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao-Stênio.pdf: 814092 bytes, checksum: b35ab296b97a20fb9607af8834520116 (MD5) Previous issue date: 2015-12-03
CAPES
As distribuições generalizadas têm sido amplamente estudadas na Estatística e diversos autores têm investigado novas distribuições de sobrevivência devido a sua flexibilidade para ajustar dados. Neste trabalho um novo método de compor distribuições é proposto: a família Half-Normal-G, em que G e chamada distribuição baseline. Demostramos que as funções densidades das distribuiçõess propostas podem ser expressas como combinação linear de funções densidades das respectivas exponencializadas-G. Diversas propriedades dessa família são estudadas. Apresentamos também uma nova distribuição de probabilidade baseado na Família de Distribuições Generalizadas Kumaraswamy (kw- G), j a conhecida na literatura. Escolhemos como baseline a distribuição Nadarajah- Haghighi, recentemente estudada por Nadarajah e Haghighi (2011) e que desenvolveram algumas propriedades interessantes. Estudamos várias propriedades da nova distribuição Kumaraswamu-Nadarajah-Haghighi (Kw-NH) e fizemos duas aplicações de bancos de dados mostrando empiricamente a flexibilidade do modelo.
APA, Harvard, Vancouver, ISO, and other styles
3

Kam, Po-ling, and 甘寶玲. "Mixture autoregression with heavy-tailed conditional distribution." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29614922.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wei, Yan. "Robust mixture regression models using t-distribution." Kansas State University, 2012. http://hdl.handle.net/2097/14110.

Full text
Abstract:
Master of Science
Department of Statistics
Weixin Yao
In this report, we propose a robust mixture of regression based on t-distribution by extending the mixture of t-distributions proposed by Peel and McLachlan (2000) to the regression setting. This new mixture of regression model is robust to outliers in y direction but not robust to the outliers with high leverage points. In order to combat this, we also propose a modified version of the proposed method, which fits the mixture of regression based on t-distribution to the data after adaptively trimming the high leverage points. We further propose to adaptively choose the degree of freedom for the t-distribution using profile likelihood. The proposed robust mixture regression estimate has high efficiency due to the adaptive choice of degree of freedom. We demonstrate the effectiveness of the proposed new method and compare it with some of the existing methods through simulation study.
APA, Harvard, Vancouver, ISO, and other styles
5

Xing, Yanru. "Robust mixture regression model fitting by Laplace distribution." Kansas State University, 2013. http://hdl.handle.net/2097/16534.

Full text
Abstract:
Master of Science
Department of Statistics
Weixing Song
A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method.
APA, Harvard, Vancouver, ISO, and other styles
6

Liu, Yantong. "Robust mixture linear EIV regression models by t-distribution." Kansas State University, 2012. http://hdl.handle.net/2097/15157.

Full text
Abstract:
Master of Science
Department of Statistics
Weixing Song
A robust estimation procedure for mixture errors-in-variables linear regression models is proposed in the report by assuming the error terms follow a t-distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the t-distribution is a scale mixture of normal distribution and a Gamma distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies. Comparison is also made with the MLE procedure under normality assumption.
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Jingyi. "Robust mixture regression modeling with Pearson type VII distribution." Kansas State University, 2013. http://hdl.handle.net/2097/15648.

Full text
Abstract:
Master of Science
Department of Statistics
Weixing Song
A robust estimation procedure for parametric regression models is proposed in the paper by assuming the error terms follow a Pearson type VII distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the Pearson type VII distributions are a scale mixture of a normal distribution and a Gamma distribution. A trimmed version of proposed procedure is also discussed in this paper, which can successfully trim the high leverage points away from the data. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in the literature.
APA, Harvard, Vancouver, ISO, and other styles
8

Karaiskos, Ilias-Efstratios. "Spray structure and mixture distribution in direct-injection gasoline engines." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417137.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kampanis, Nicholas. "Flow, mixture distribution and combustion in five-valve gasoline engines." Thesis, Imperial College London, 2003. http://hdl.handle.net/10044/1/8338.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Assis, Alice Nascimento de, and 92-99331-6592. "Um modelo multivariado para predição de taxas e proporções dependentes." Universidade Federal do Amazonas, 2018. https://tede.ufam.edu.br/handle/tede/6391.

Full text
Abstract:
Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-05-22T13:53:20Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) versaofinal.pdf: 8756608 bytes, checksum: e4b5f21e17776e8f9af04b6752317a59 (MD5)
Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-05-22T14:16:29Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) versaofinal.pdf: 8756608 bytes, checksum: e4b5f21e17776e8f9af04b6752317a59 (MD5)
Made available in DSpace on 2018-05-22T14:16:29Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) versaofinal.pdf: 8756608 bytes, checksum: e4b5f21e17776e8f9af04b6752317a59 (MD5) Previous issue date: 2018-03-09
CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Relative humidity interferes in many aspects in the life of the human being, and due to the many consequences that a low or a high percentage can entail, the control of its level is of paramount importance. Thus, the modeling of extreme situations of this variable can aid in the planning of human activities that are susceptible to their harmful effects, such as public health. The main interest is to predict, based on probability density functions applied to observed data, the values that may occur in a certain locality. The Generalized Distribution of Extreme Values has been widely used for this purpose and research using Time Series analysis of meteorological and climatic data. In this work, a statistical model is proposed for prediction of rates and temporal proportions and/or spatially dependents. The model was constructed by marginalizing the Kumaraswamy G-exponentialised distribution conditioned to a random field with positive alpha-stable distribution. Some properties of this model were presented, procedures for estimation and inference were discussed and an MCEM algorithm was developed to estimate the parameters. As a particular case, the model was used for spatial prediction of relative humidity in weather stations at Amazonas state, Brazil.
A umidade relativa interfere em vários aspectos na vida do ser humano, e devido as muitas consequências que um baixo ou um alto percentual podem acarretar, o controle de seu nível é de suma importância. Dessa forma, a modelagem de situações extremas dessa variável pode auxiliar no planejamento de atividades humanas que sejam suscetíveis aos seus efeitos danosos, como a saúde pública. O principal interesse é prever com base em funções densidade de probabilidade aplicadas aos dados observados, os valores que possam ocorrer em uma certa localidade. A distribuição Generalizada de Valores Extremos tem sido amplamente utilizada com essa finalidade e pesquisas utilizando análise de Séries Temporais de dados meteorológicos e climáticos. Neste trabalho, é proposto um modelo estatístico para predição de taxas e proporções temporais e/ou espacialmente dependentes. O modelo foi construído através da marginalização da distribuição Kumaraswamy G-exponencializada condicionada a um campo aleatório com distribuição alfaestável positivo. Algumas propriedades desse modelo foram apresentadas, procedimentos para estimação e inferência foram discutidos e um algoritmo MCEM foi desenvolvido parar estimar os parâmetros. Como um caso particular, o modelo foi utilizado para predição espacial da umidade relativa do ar observada nas estações meteorológicas do Estado do Amazonas.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Mixture of Kumaraswamy distribution"

1

von Davier, Matthias. Multivariate and Mixture Distribution Rasch Models. Edited by Claus H. Carstensen. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Linear approximations in convex metric spaces and the application in the mixture theory of probability theory. Singapore: World Scientific, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Teikari, Ismo. Poisson mixture sampling in controlling the distribution of response burden in longitudinal and cross section business surveys. Helsinki: Helsinki School of Economics and Business Administration, 2001.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Multivariate And Mixture Distribution Rasch Models Extensions And Applications. Springer, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cheng, Russell. Finite Mixture Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0017.

Full text
Abstract:
Fitting a finite mixture model when the number of components, k, is unknown can be carried out using the maximum likelihood (ML) method though it is non-standard. Two well-known Bayesian Markov chain Monte Carlo (MCMC) methods are reviewed and compared with ML: the reversible jump method and one using an approximating Dirichlet process. Another Bayesian method, to be called MAPIS, is examined that first obtains point estimates for the component parameters by the maximum a posteriori method for different k and then estimates posterior distributions, including that for k, using importance sampling. MAPIS is compared with ML and the MCMC methods. The MCMC methods produce multimodal posterior parameter distributions in overfitted models. This results in the posterior distribution of k being biased towards high k. It is shown that MAPIS does not suffer from this problem. A simple numerical example is discussed.
APA, Harvard, Vancouver, ISO, and other styles
6

Cheng, Russell. Finite Mixture Examples; MAPIS Details. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0018.

Full text
Abstract:
Two detailed numerical examples are given in this chapter illustrating and comparing mainly the reversible jump Markov chain Monte Carlo (RJMCMC) and the maximum a posteriori/importance sampling (MAPIS) methods. The numerical examples are the well-known galaxy data set with sample size 82, and the Hidalgo stamp issues thickness data with sample size 485. A comparison is made of the estimates obtained by the RJMCMC and MAPIS methods for (i) the posterior k-distribution of the number of components, k, (ii) the predictive finite mixture distribution itself, and (iii) the posterior distributions of the component parameters and weights. The estimates obtained by MAPIS are shown to be more satisfactory and meaningful. Details are given of the practical implementation of MAPIS for five non-normal mixture models, namely: the extreme value, gamma, inverse Gaussian, lognormal, and Weibull. Mathematical details are also given of the acceptance-rejection importance sampling used in MAPIS.
APA, Harvard, Vancouver, ISO, and other styles
7

Piepel, Gregory Frank. Models and designs for generalizations of mixture experiments where the response depends on the total amount. 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Davier, Matthias von, and Claus H. Carstensen. Multivariate and Mixture Distribution Rasch Models: Extensions and Applications (Statistics for Social and Behavioral Sciences). Springer, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Lin, Li, He Guoqi, and United States. National Aeronautics and Space Administration., eds. Nonlinear spectral mixture modeling of lunar multispectral: Implications for lateral transport. [Washington, DC: National Aeronautics and Space Administration, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

(Editor), Matthias von Davier, and Claus H. Carstensen (Editor), eds. Multivariate and Mixture Distribution Rasch Models: Extensions and Applications (Statistics for Social Science and Behavorial Sciences). Springer, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Mixture of Kumaraswamy distribution"

1

Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Mixture Distribution." In Encyclopedia of Machine Learning, 680. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_546.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Rost, Jürgen, and Matthias von Davier. "Mixture Distribution Rasch Models." In Rasch Models, 257–68. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

von Davier, Matthias, and Kentaro Yamamoto. "Mixture-Distribution and HYBRID Rasch Models." In Multivariate and Mixture Distribution Rasch Models, 99–115. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kelderman, Henk. "Loglinear Multivariate and Mixture Rasch Models." In Multivariate and Mixture Distribution Rasch Models, 77–97. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Crespo-Roces, David, Iván Méndez-Jiménez, Sancho Salcedo-Sanz, and Miguel Cárdenas-Montes. "Generalized Probability Distribution Mixture Model for Clustering." In Lecture Notes in Computer Science, 251–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92639-1_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Carstensen, Claus H., and Jürgen Rost. "Multidimensional Three-Mode Rasch Models." In Multivariate and Mixture Distribution Rasch Models, 157–75. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kreiner, Svend, and Karl Bang Christensen. "Validity and Objectivity in Health-Related Scales: Analysis by Graphical Loglinear Rasch Models." In Multivariate and Mixture Distribution Rasch Models, 329–46. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

von Davier, Matthias, Jürgen Rost, and Claus H. Carstensen. "Introduction: Extending the Rasch Model." In Multivariate and Mixture Distribution Rasch Models, 1–12. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Formann, Anton K. "(Almost) Equivalence Between Conditional and Mixture Maximum Likelihood Estimates for Some Models of the Rasch Type." In Multivariate and Mixture Distribution Rasch Models, 177–89. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Meiser, Thorsten. "Rasch Models for Longitudinal Data." In Multivariate and Mixture Distribution Rasch Models, 191–99. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Mixture of Kumaraswamy distribution"

1

Özel, Gamze. "Bivariate Kumaraswamy distribution with an application on earthquake data." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912844.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Simbolon, H. G., I. Fithriani, and S. Nurrohmah. "Estimation of shape β parameter in Kumaraswamy distribution using Maximum Likelihood and Bayes method." In INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2016 (ISCPMS 2016): Proceedings of the 2nd International Symposium on Current Progress in Mathematics and Sciences 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4991264.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Sarma, Prathusha K., and Tarunraj Singh. "A mixture distribution for visual foraging." In ETRA '14: Eye Tracking Research and Applications. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2578153.2578210.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Yang, Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, and Shu-Tao Xia. "Self -Paced Mixture of T Distribution Model." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462323.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chauhan, P. S., Sandeep Kumar, S. K. Soni, V. K. Upaddhaya, and D. Pant. "Average Channel Capacity over Mixture Gamma Distribution." In 2020 International Conference on Electrical and Electronics Engineering (ICE3). IEEE, 2020. http://dx.doi.org/10.1109/ice348803.2020.9122966.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gudnason, Jon, and Mike Brookes. "Distribution based classification using Gaussian Mixture Models." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745576.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Gudnason and Brookes. "Distribution based classification using Gaussian mixture models." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004837.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Blacknell, David. "Mixture distribution model for correlated SAR clutter." In Satellite Remote Sensing III, edited by Giorgio Franceschetti, Christopher J. Oliver, Franco S. Rubertone, and Shahram Tajbakhsh. SPIE, 1996. http://dx.doi.org/10.1117/12.262719.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ito, Akinori. "Recognition of sounds using square cauchy mixture distribution." In 2016 IEEE International Conference on Signal and Image Processing (ICSIP). IEEE, 2016. http://dx.doi.org/10.1109/siprocess.2016.7888359.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Elkotby, Hussain, and Mai Vu. "A Mixture Model for NLOS mmWave Interference Distribution." In GLOBECOM 2016 - 2016 IEEE Global Communications Conference. IEEE, 2016. http://dx.doi.org/10.1109/glocom.2016.7841515.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Mixture of Kumaraswamy distribution"

1

Abdel-Hamid, Alaa H., and Atef F. Hashem. A New Compound Distribution Based on a Mixture of Distributions and a Mixed System. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, November 2018. http://dx.doi.org/10.7546/crabs.2018.11.01.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chernoff, Herman, and Eric Lander. Asymptotic Distribution of the Likelihood Ratio Test That a Mixture of Two Binomials is a Single Binomial. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada236714.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Grossman, A., and K. Grant. A correlated K-distribution model of the heating rates for H[sub 2]O and a molecular mixture in the 0-2500 cm[sup [minus]1] wavelength region in the atmosphere between 0 and 60 km. Office of Scientific and Technical Information (OSTI), November 1992. http://dx.doi.org/10.2172/7025988.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Lee, Jusang, John E. Haddock, Dario D. Batioja Alvarez, and Reyhaneh Rahbar Rastegar. Quality Control and Quality Assurance of Asphalt Mixtures Using Laboratory Rutting and Cracking Tests. Purdue University, 2019. http://dx.doi.org/10.5703/1288284317087.

Full text
Abstract:
The main objectives of this project were to review the available balanced-mix design (BMD) methodologies, understand the I-FIT and Hamburg Wheel Tracking Test (HWTT) test methods using INDOT asphalt mixtures, and to explore the application of these tests to both a BMD approach and as performance-related Quality Control (QC) and Quality Acceptance (QA) methods. Two QA mixture specimen types, plant-mixed laboratory-compacted (PMLC) and plant-mixed field-compacted (PMFC) were used in the determination of cracking and rutting parameters. Distribution functions for the flexibility index (FI) values and rutting parameters were determined for various mixture types. The effects of specimen geometry and air voids contents on the calculated Flexibility Index (FI) and rutting parameters were investigated. The fatigue characteristics of selected asphalt mixtures were determined using the S-VECD test according to different FI levels for different conditions. A typical full-depth pavement section was implemented in FlexPAVE to explore the cracking characteristics of INDOT asphalt mixtures by investigating the relationship between the FI values of QA samples with the FlexPAVE pavement performance predictions. The FI values obtained from PMFC specimens were consistently higher than their corresponding PMLC specimens. This study also found that FI values were affected significantly by variations in specimen thickness and air voids contents, having higher FI values with higher air voids contents and thinner specimens. These observations do not agree with the general material-performance expectations that better cracking resistance is achieved with lower air voids content and thicker layers. Additionally, PG 70-22 mixtures show the lowest mean FI values followed by the PG 76-22 and 64-22 mixtures. The same order was observed from the ΔTc (asphalt binder cracking index) of INDOT’s 2017 and 2018 projects. Finally, it was found that the HWTT showed reasonable sensitivity to the different characteristics (e.g., aggregate sizes, binder types, and air voids contents) of asphalt mixtures. Mixtures containing modified asphalt binders showed better rut resistance and higher Rutting Resistance Index (RRI) than those containing unmodified binders.
APA, Harvard, Vancouver, ISO, and other styles
5

Ley, M., Zane Lloyd, Shinhyu Kang, and Dan Cook. Concrete Pavement Mixtures with High Supplementary Cementitious Materials Content: Volume 3. Illinois Center for Transportation, September 2021. http://dx.doi.org/10.36501/0197-9191/21-032.

Full text
Abstract:
Fly ash is a by-product of coal combustion, made up of particles that are collected through various methods. This by-product has been used successfully as a partial Portland cement replacement in concrete, but the performance predictions of fly ash in concrete have been difficult to predict, especially at high fly ash replacement rates. This study focuses on comparing the performance of concrete with a variety of fly ash mixtures as well as the particle distribution and chemical makeup of fly ash. The slump, unit weight, compressive strength, and isothermal calorimetry tests were used to measure the performance of concrete at 0%, 20%, and 40% fly ash replacement levels. The particle distribution of fly ash was measured with an automated scanning electron microscope. Additionally, the major and minor oxides from the chemical makeup of fly ash were measured for each mixture and inputted into a table. The particle distribution and chemical makeup of fly ash were compared to the performance of slump, unit weight, compressive strength, isothermal calorimetry, and surface electrical resistivity.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Mixture of Kumaraswamy distribution' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography