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Relevant bibliographies by topics / Bivariate normal distribution

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Academic literature on the topic 'Bivariate normal distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Bivariate normal distribution"

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

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Goto, Masashi, and Toshimitsu Hamasaki. "THE BIVARIATE POWER-NORMAL DISTRIBUTION." Bulletin of informatics and cybernetics 34, no. 1 (2002): 29–49. http://dx.doi.org/10.5109/13507.

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Pratikno, B., Jajang, A. C. C. Putri, and Y. W. W. Haryono. "The Bivariate Central Normal Distribution." Journal of Physics: Conference Series 824 (April 18, 2017): 012030. http://dx.doi.org/10.1088/1742-6596/824/1/012030.

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Kocherlakota, Subrahmaniam, and Kathleen Kocherlakota. "Process capability index: bivariate normal distribution." Communications in Statistics - Theory and Methods 20, no. 8 (1991): 2529–47. http://dx.doi.org/10.1080/03610929108830648.

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Ahsanullah, Mohammad, N. Bansal, G. G. Hamedani, and Hao Zhang. "A Note on Bivariate Normal Distribution." Calcutta Statistical Association Bulletin 48, no. 3-4 (1998): 237–40. http://dx.doi.org/10.1177/0008068319980311.

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Louzada, Francisco, Anderson Ara, and Guilherme Fernandes. "The bivariate alpha-skew-normal distribution." Communications in Statistics - Theory and Methods 46, no. 14 (2017): 7147–56. http://dx.doi.org/10.1080/03610926.2015.1024865.

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Mahmoudi, Eisa, and Hamed Mahmoodian. "A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions." Journal of Statistical Theory and Applications 16, no. 2 (2017): 198. http://dx.doi.org/10.2991/jsta.2017.16.2.5.

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Mønness, Erik. "The bivariate power-normal distribution and the bivariate Johnson system bounded distribution in forestry, including height curves." Canadian Journal of Forest Research 45, no. 3 (2015): 307–13. http://dx.doi.org/10.1139/cjfr-2014-0333.

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A bivariate diameter and height distribution yields a unified model of a forest stand. The bivariate Johnson system bounded distribution and the bivariate power-normal distribution are explored. The power-normal distribution originates from the well-known Box–Cox transformation. As evaluated by the bivariate Kolmogorov–Smirnov distance, the bivariate power-normal distribution seems to be superior to the bivariate Johnson system bounded distribution. The conditional median height given the diameter is a possible height curve and is compared with a simple hyperbolic height curve. Evaluated by th

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Monhor, Davaadorjin. "INEQUALITIES FOR CORRELATED BIVARIATE NORMAL DISTRIBUTION FUNCTION." Probability in the Engineering and Informational Sciences 27, no. 1 (2012): 115–23. http://dx.doi.org/10.1017/s0269964812000332.

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We establish several such new inequalities that improve the accuracy of bounding the probability content of bivariate normal distribution function by the Slepian's inequalities. Apart from the improvement of the accuracy, their easy–to–compute form is another attractive feature of these new inequalities which fact is of considerable interest for both theoretical and practical applications. A novel application possibility is mentioned, too.

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Willink, R. "Bounds on the Bivariate Normal Distribution Function." Communications in Statistics - Theory and Methods 33, no. 10 (2005): 2281–97. http://dx.doi.org/10.1081/sta-200031505.

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Dissertations / Theses on the topic "Bivariate normal distribution"

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Shi, Yipin. "Study on Bivariate Normal Distribution." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/745.

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Let (X, Y) be bivariate normal random vectors which represent the responses as a result of Treatment 1 and Treatment 2. The statistical inference about the bivariate normal distribution parameters involving missing data with both treatment samples is considered. Assuming the correlation coefficient ρ of the bivariate population is known, the MLE of population means and variance (ξ, η, and σ2) are obtained. Inferences about these parameters are presented. Procedures of constructing confidence interval for the difference of population means ξ – η and testing hypothesis about ξ – η are establishe

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Qumsiyeh, Sahar Botros. "Non-normal Bivariate Distributions: Estimation And Hypothesis Testing." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608941/index.pdf.

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When using data for estimating the parameters in a bivariate distribution, the tradition is to assume that data comes from a bivariate normal distribution. If the distribution is not bivariate normal, which often is the case, the maximum likelihood (ML) estimators are intractable and the least square (LS) estimators are inefficient. Here, we consider two independent sets of bivariate data which come from non-normal populations. We consider two distinctive distributions: the marginal and the conditional distributions are both Generalized Logistic, and the marginal and conditional distributio

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Orjuela, Maria del Pilar. "A Study on the Correlation of Bivariate And Trivariate Normal Models." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/976.

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Suppose two or more variables are jointly normally distributed. If there is a common relationship between these variables it would be very important to quantify this relationship by a parameter called the correlation coefficient which measures its strength, and the use of it can develop an equation for predicting, and ultimately draw testable conclusion about the parent population. This research focused on the correlation coefficient ρ for the bivariate and trivariate normal distribution when equal variances and equal covariances are considered. Particularly, we derived the maximum Likelihood

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Eren, Emrah. "Effect Of Estimation In Goodness-of-fit Tests." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12611046/index.pdf.

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In statistical analysis, distributional assumptions are needed to apply parametric procedures. Assumptions about underlying distribution should be true for accurate statistical inferences. Goodness-of-fit tests are used for checking the validity of the distributional assumptions. To apply some of the goodness-of-fit tests, the unknown population parameters are estimated. The null distributions of test statistics become complicated or depend on the unknown parameters if population parameters are replaced by their estimators. This will restrict the use of the test. Goodness-of-fit statistics whi

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Raposo, Antonio Adolpho Martins. "Alocação de Medidores para a Estimação de Estado em Redes Elétricas Inteligentes." Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/299.

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Made available in DSpace on 2016-08-17T14:52:40Z (GMT). No. of bitstreams: 1 Dissertacao-AntonioAdolphoMartinsRaposo.pdf: 6219934 bytes, checksum: 92f0e1fb7c3d703fcf27aae305b549f2 (MD5) Previous issue date: 2016-02-26<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>To plan and operate properly a Smart Grid (SG), many new technical considerations in the context of distribution systems, must be considered, for example: stability (due to installation of Distributed Generation (DG), the load and generation dispatch, management of energy storage devices and the assessment of t

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Martin, Thomas Newton. "Modelo estocástico para estimação da produtividade de soja no Estado de São Paulo utilizando simulação normal bivariada." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11136/tde-20032007-161308/.

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A disponibilidade de recursos, tanto de ordem financeira quanto de mão-de-obra, é escassa. Sendo assim, deve-se incentivar o planejamento regional que minimize a utilização de recursos. A previsão de safra por intermédio de técnicas de modelagem deve ser realizada anteriormente com base nas características regionais, indicando assim as diretrizes básicas da pesquisa, bem como o planejamento regional. Dessa forma, os objetivos deste trabalho são: (i) caracterizar as variáveis do clima por intermédio de diferentes distribuições de probabilidade; (ii) verificar a homogeneidade espacial e temporal

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蕭惠玲. "Compatibility of normal conditional distributions under bivariate distribution." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/21546500527704428495.

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碩士<br>國立政治大學<br>應用數學系數學教學碩士在職專班<br>98<br>Arnold and Press (1989) provide the theory about the compatibility of two conditional densities. In this research, we use their results to find the sufficient conditions of the compatibility of two conditional densities, which have the normal form. New sufficient conditions are also given if we further assume that corresponding joint density is normal. In addition, we use computer to generate two different samples from two different conditional normal distributions, which are from two different joint normal distributions. With the repeated samples, we p

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Huang, Fang-chi, and 黃芳琪. "APPLYING BIVARIATE NORMAL DISTRIBUTION TO CONSTRUCT DEMAND MODEL FOR TRAVEL PRODUCT." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/14127155571219455577.

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博士<br>南華大學<br>企業管理系管理科學碩博士班<br>99<br>This study attempts to construct a demand model of bivariate normal distribution function to consider willing to pay and transaction cost for travel product. We discuss the ratio of transaction cost which is faced by supplier and consumer that has effect on demand model. The results indicated when the gain of supplier is above two times of the consumer surplus, the higher ratio of transaction cost faced by consumer, the higher demand of travel product would be. 　　　Meanwhile, we try to present the pricing strategies on growing income inequality. The consume

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Onyancha, Nyambane Bosire. "An investigation into undergraduate student's difficulties in learning the bivariate normal distribution : a case of a Kenyan university." Diss., 2017. http://hdl.handle.net/10500/23588.

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The low grades that students score in some statistical units in Kenyan universities is of great concern and has evoked research interest in the teaching of some of the units and the students’ learning of the statistical content. The aim of the study was to investigate the difficulties undergraduate students experience in the learning of bivariate normal distribution in a Kenyan university. The research also aimed to answer the following research questions on the difficulties undergraduate students encounter in the learning of bivariate normal distribution. The first research question wa

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Fong, Yu-Syuan, and 馮鈺軒. "Better tests for the correlation coefficient in a bivariate normal distribution and related studies of their robustness against skew normality." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/87065232915558512921.

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碩士<br>國立東華大學<br>應用數學系<br>98<br>We consider the testing problem, one-sided or two-sided, about the correlation coefficient ρ of a bivariate normal distribution. This is not a new inferential problem and good tests exist in the literature, for example, Lehmann (1994) gives the corresponding UMPU and UMPI tests which are based on the sample correlation coefficient R. However, for the alternative H1:ρ&gt;ρ0 with ρ0≠0, the tests above are not easy to implement since their critical points need to be calculated numerically, due to the complicated density function of R. Significantly establish H1:

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Books on the topic "Bivariate normal distribution"

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Ge, Elyse. Numerical computation of bivariate and trivariate normal integral. 1994.

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Book chapters on the topic "Bivariate normal distribution"

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Balakrishna, N., and Chin Diew Lai. "Bivariate Normal Distribution." In Continuous Bivariate Distributions. Springer New York, 2009. http://dx.doi.org/10.1007/b101765_12.

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Zelterman, Daniel. "Bivariate Normal Distribution." In Applied Multivariate Statistics with R. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14093-3_6.

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Tong, Y. L. "The Bivariate Normal Distribution." In Springer Series in Statistics. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4613-9655-0_2.

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Das, N. C. "Univariate Normal Distribution and Its Quantile." In Decision Processes by Using Bivariate Normal Quantile Pairs. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2364-1_3.

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Das, N. C. "Bivariate Normal Distribution and Heuristic-Algorithm of BIVNOR for Generating Biquantile Pairs." In Decision Processes by Using Bivariate Normal Quantile Pairs. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2364-1_4.

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Jolicoeur, Pierre. "The bivariate normal distribution and the correlation coefficient, r." In Introduction to Biometry. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4777-8_20.

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Ghosh, Malay, Upasana Santra, and Dalho Kim. "Probability matching priors for some parameters of the bivariate normal distribution." In Institute of Mathematical Statistics Collections. Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/074921708000000066.

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Thomopoulos, Nick T. "Bivariate Normal." In Statistical Distributions. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65112-5_19.

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Thomopoulos, Nick T. "Bivariate Normal." In Probability Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76042-1_8.

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Vanitha, Kakollu, and P. Chandrasekhar Reddy. "Toward Segmentation of Images Based on Non-Normal Mixture Models Based on Bivariate Skew Distribution." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3779-5_18.

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Conference papers on the topic "Bivariate normal distribution"

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Kurz, Gerhard, and Uwe D. Hanebeck. "Parameter estimation for the bivariate wrapped normal distribution." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402373.

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Cao, Limei, and Huafei Sun. "The geometric structure of the bivariate q-normal distribution manifold." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002511.

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Soares, Catarina S., and C. Guedes Soares. "Comparison of Bivariate Models of the Distribution of Significant Wave Height and Peak Wave Period." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29740.

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This paper presents the results of a comparison of the fit of three bivariate models to a set of 14 years of significant wave height and peak wave period data from the North Sea. One of the methods defines the joint distribution from a marginal distribution of significant wave height and a set of distributions of peak period conditional on significant wave height. Other method applies the Plackett model to the data and the third one applies the Box-Cox transformation to the data in order to make it approximately normal and then fits a bivariate normal distribution to the transformed data set.

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Tao, Shanshan, Sheng Dong, and Yinghui Xu. "Design Parameter Estimation of Wave Height and Wind Speed With Bivariate Copulas." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10519.

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The data of annual extreme wave height and corresponding wind speed at a platform in Bohai Bay is hindcasted by a numerical model from 1970 to 1993. Common-used design probability distributions, such as Gumbel distribution, Weibull distribution, and lognormal distribution are applied to fit the data of extreme wave height and concomitant wind speed, respectively. Then the best-fitted marginal distributions of annual extreme wave height and wind speed can be selected. Bivariate normal copula and Frank copula are utilized to construct joint distribution of these two random variables. Based on em

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Dukic, Darko, Gordana Dukic, and Mate Sesar. "Portfolio optimization based on a computer simulation of securities rates of return from the bivariate normal distribution." In 2008 30th International Conference on Information Technology Interfaces (ITI). IEEE, 2008. http://dx.doi.org/10.1109/iti.2008.4588407.

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Bae, Sangjune, Chanyoung Park, and Nam H. Kim. "Uncertainty Quantification of Reliability Analysis Under Surrogate Model Uncertainty Using Gaussian Process." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85541.

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The main objective of this paper is to quantify the effect of surrogate model uncertainty on reliability in addition to the aleatory randomness of the input variables, especially when Kriging surrogate model is utilized where the prediction uncertainty is modeled with a normal distribution. A novel approach is presented which requires only a single set of Monte Carlo Simulation (MCS) to precisely estimate the variance of reliability that is used as an uncertainty measure. It is found that the method only requires the bivariate cumulative distribution function, and the result shows that the unc

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Umeki, Kiyoshi, Akihiro Sumida, Tatsuyuki Seino, En-Mi Lim, and Tsuyoshi Honjo. "Fitting the Truncated Bivariate Normal Distribution to the Relationship between Diameter and Length of Current-Year Shoots in Betula Platyphylla in Hokkaido, Northern Japan." In 2006 International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA). IEEE, 2006. http://dx.doi.org/10.1109/pma.2006.15.

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VISALGA, Gintaras, Edmundas PETRAUSKAS, and Petras RUPŠYS. "METHOD FOR INCREASING AN ACCURACY OF DETECTING DECAY BY THE ARBOTOM® 3-D TREE TOMOGRAPH ON PICEA ABIES (L.) H. KARST TREES DAMAGED BY HETEROBASIDION ANNOSUM (FR.) BREF." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.057.

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ARBOTOM® 3-D tree tomograph is a non-destructive decay-sensing instrument based on measurements in standing trees. The accuracy of ARBOTOM® 3-D tree tomograph in detecting decay was evaluated for 703 standing trees in 27 Norway spruce (Picea abies) stands. Trees were measured at three different heights, 0 m, 1 m, and 2 m above the ground. Sections were later inspected for the presence of decay by increment borings and wood croscuts. A system of stochastic differential equations and multivariate normal copula density function were used to develop decay detecting rule. In the present study, the

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Vanem, Erik. "Copula-Based Bivariate Modelling of Significant Wave Height and Wave Period and the Effects of Climate Change on the Joint Distribution." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54314.

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The joint distribution of several met-ocean variables is required for risk assessment and load and response calculations in marine engineering. For example, a joint description is needed to construct environmental contours for probabilistic structural reliability analyses. Typically, the joint distribution of significant wave height and wave period is required as a minimum. This paper presents a study on various bivariate modelling techniques for the joint distribution of significant wave height and zero-crossing wave period, i.e. a conditional model, a bi-variate log-normal model and several

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Zhang, Jie, Souma Chowdhury, Achille Messac, and Luciano Castillo. "A Comprehensive Measure of the Energy Resource Potential of a Wind Farm Site." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54677.

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Currently, the quality of wind measure of a site is assessed using Wind Power Density (WPD). This paper proposes to use a more credible metric namely, one we call the Wind Power Potential (WPP). While the former only uses wind speed information, the latter exploits both wind speed and wind direction distributions, and yields more credible estimates. The new measure of quality of a wind resource, the Wind Power Potential Evaluation (WPPE) model, investigates the effect of wind velocity distribution on the optimal net power generation of a farm. Bivariate normal distribution is used to character

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