Relevant bibliographies by topics / Bivariate non-Gaussian data

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Academic literature on the topic 'Bivariate non-Gaussian data'

Author: Grafiati
Published: 28 May 2022
Last updated: 30 July 2025

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Journal articles on the topic "Bivariate non-Gaussian data"

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Basawa, I. V., and J. Zhou. "Non-Gaussian bifurcating models and quasi-likelihood estimation." Journal of Applied Probability 41, A (2004): 55–64. http://dx.doi.org/10.1239/jap/1082552190.

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A general class of Markovian non-Gaussian bifurcating models for cell lineage data is presented. Examples include bifurcating autoregression, random coefficient autoregression, bivariate exponential, bivariate gamma, and bivariate Poisson models. Quasi-likelihood estimation for the model parameters and large-sample properties of the estimates are discussed.
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Basawa, I. V., and J. Zhou. "Non-Gaussian bifurcating models and quasi-likelihood estimation." Journal of Applied Probability 41, A (2004): 55–64. http://dx.doi.org/10.1017/s0021900200112203.

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A general class of Markovian non-Gaussian bifurcating models for cell lineage data is presented. Examples include bifurcating autoregression, random coefficient autoregression, bivariate exponential, bivariate gamma, and bivariate Poisson models. Quasi-likelihood estimation for the model parameters and large-sample properties of the estimates are discussed.
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3

Marra, Giampiero, and Rosalba Radice. "A joint regression modeling framework for analyzing bivariate binary data in R." Dependence Modeling 5, no. 1 (2017): 268–94. http://dx.doi.org/10.1515/demo-2017-0016.

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Abstract We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological detail
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Monahan, A. H., and T. DelSole. "Information theoretic measures of dependence, compactness, and non-gaussianity for multivariate probability distributions." Nonlinear Processes in Geophysics 16, no. 1 (2009): 57–64. http://dx.doi.org/10.5194/npg-16-57-2009.

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Abstract. A basic task of exploratory data analysis is the characterisation of "structure" in multivariate datasets. For bivariate Gaussian distributions, natural measures of dependence (the predictive relationship between individual variables) and compactness (the degree of concentration of the probability density function (pdf) around a low-dimensional axis) are respectively provided by ordinary least-squares regression and Principal Component Analysis. This study considers general measures of structure for non-Gaussian distributions and demonstrates that these can be defined in terms of the
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Chatrabgoun, O., A. Hosseinian-Far, and A. Daneshkhah. "Constructing gene regulatory networks from microarray data using non-Gaussian pair-copula Bayesian networks." Journal of Bioinformatics and Computational Biology 18, no. 04 (2020): 2050023. http://dx.doi.org/10.1142/s0219720020500237.

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Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality, multi-modality and heavy tailedness that are commonly seen in current massive genetic data. To relax this limitative constraint, one can apply copula function which is a multivariate cumulative
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Bladt, Martin, and Alexander J. McNeil. "Time series with infinite-order partial copula dependence." Dependence Modeling 10, no. 1 (2022): 87–107. http://dx.doi.org/10.1515/demo-2022-0105.

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Abstract Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious me
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Cipollone, Andrea, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina. "Bivariate sea-ice assimilation for global-ocean analysis–reanalysis." Ocean Science 19, no. 5 (2023): 1375–92. http://dx.doi.org/10.5194/os-19-1375-2023.

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Abstract. In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nevertheless, while univariate algorithms are commonly used to constrain sea-ice area or volume, multivariate approaches have not yet been employed due to the highly non-Gaussian distribution of sea-ice variables together with the low accuracy of thickness observations. This study extends a 3
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Rinadi, Geraldus Anggoro, Leopoldus Ricky Sasongko, and Bambang Susanto. "Regresi Median Pada Copula Bivariat." JTAM | Jurnal Teori dan Aplikasi Matematika 3, no. 1 (2019): 07. http://dx.doi.org/10.31764/jtam.v3i1.728.

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Abstrak: Analisis regresi adalah analisis yang sering digunakan dalam segala bidang yang bertujuan untuk memodelkan hubungan antara dua jenis variabel tak bebas dengan satu atau variabel bebas. Regresi linier masih memiliki beberapa kekurangan, maka dari untuk mengatasinya dengan regresi median. Copula dapat digunakan untuk mendeteksi hubungan data bivariat dengan peubah-peubah yang berbeda. Hasil penelitian menunjukkan kurva kuantil bersyarat terbaik berdasarkan MSE terkecil Data I yaitu copula Plackett sebesar 0.8650. Sedangkan nilai MSE terkecil Data II yaitu copula Gaussian sebesar 0.3954.
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9

Pires, Carlos A., and Rui A. P. Perdigão. "Non-Gaussianity and Asymmetry of the Winter Monthly Precipitation Estimation from the NAO." Monthly Weather Review 135, no. 2 (2007): 430–48. http://dx.doi.org/10.1175/mwr3407.1.

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Abstract The present work assesses non-Gaussianity and asymmetry within the statistical response of the monthly winter (December–February) precipitation to the North Atlantic Oscillation (NAO) over the North Atlantic–European region (NAE). To evaluate asymmetry, data are split through the median of the NAO index and side correlations are computed for each regime [negative and positive phases of the NAO (NAO− and NAO+, respectively)]. The following statistically significant differences between these correlations are found: (a) near the central North Atlantic, around 40°N, 20°W, and southeast of
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10

Aldhufairi, Fadal Abdullah Ali, and Jungsywan H. Sepanski. "New Bivariate Copulas via Lomax Distribution Generated Distortions." AppliedMath 4, no. 2 (2024): 641–65. http://dx.doi.org/10.3390/appliedmath4020035.

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We develop a framework for creating distortion functions that are used to construct new bivariate copulas. It is achieved by transforming non-negative random variables with Lomax-related distributions. In this paper, we apply the distortions to the base copulas of independence, Clayton, Frank, and Gumbel copulas. The properties of the tail dependence coefficient, tail order, and concordance ordering are explored for the new families of distorted copulas. We conducted an empirical study using the daily net returns of Amazon and Google stocks from January 2014 to December 2023. We compared the p
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Dissertations / Theses on the topic "Bivariate non-Gaussian data"

1

Prykhodko, S., N. Prykhodko, L. Makarova, O. Kudin, T. Smykodub, and A. Prykhodko. "Detecting bivariate outliers on the basis of normalizing transformations for non-Gaussian data." Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/55754.

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The statistical technique for detecting outliers in bivariate non-Gaussian data on the basis of normalizing transformations, prediction ellipse and a test statistic (TS) for the Mahalanobis squared distance (MSD), which has an approximate F distribution, is proposed. Application of the technique is considered for detecting outliers in two bivariate non-Gaussian data sets: the first, actual effort (hours) and size (adjusted function points) from 145 maintenance and development projects, the second, effort (hours) and mass (tonnes) of designed the section of the ship from 188 designs
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