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

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

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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2

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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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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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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Xiao, Bo, Claus Haslauer, and Geoffrey Bohling. "Comparison of Multivariate Spatial Dependence Structures of DPIL and Flowmeter Hydraulic Conductivity Data Sets at the MADE Site." Water 11, no. 7 (2019): 1420. http://dx.doi.org/10.3390/w11071420.

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We analyse two datasets of hydraulic conductivity (K) from the MAcroDispersion Experiment (MADE) site, one measured by direct-push injection logging (DPIL) and the other by flowmeter profiling. The analysis is performed using copula techniques which do not rely on the assumption of multivariate Gaussianity and provide a means to characterise differing degrees of spatial dependence in different quantiles of the K distribution. This characterisation provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and the tradition

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Bolbolian Ghalibaf, Mohammad. "Relationship Between Kendall's tau Correlation and Mutual Information." Revista Colombiana de Estadística 43, no. 1 (2020): 3–20. http://dx.doi.org/10.15446/rce.v43n1.78054.

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Mutual information (MI) can be viewed as a measure of multivariate association in a random vector. However, the estimation of MI is difficult since the estimation of the joint probability density function (PDF) of non Gaussian distributed data is a hard problem. Copula function is an appropriate tool for estimating MI since the joint probability density function ofrandom variables can be expressed as the product of the associated copula density function and marginal PDF’s. With a little search, we find that the proposed copulas-based mutual information is much more accurate than conventional m

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KUNDU, DAMITRI, and KIRANMOY DAS. "A quantile-regression approach to bivariate longitudinal joint modeling." Journal of Statistical Research 58, no. 1 (2024): 111–30. http://dx.doi.org/10.3329/jsr.v58i1.75417.

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Joint modeling of longitudinal outcomes and time-to-event data has become a major re- search interest in the last thirty years. A joint model is useful since it helps (i) to under- stand the evolution of the outcome(s) of interest over time, (ii) to understand the effects of the outcomes on the time of occurrence of some event(s) of interest (e.g. death/relapse), and (iii) to study the effects of the time-varying and time-invariant predictors on the longitudinal and time-to-event process. Traditional linear mixed models are routinely used for modeling the longitudinal process. However, for non

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Kong, Liulin, Heng Li, Bo Zhang, and Hanbin Luo. "Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations." Advances in Civil Engineering 2021 (August 24, 2021): 1–18. http://dx.doi.org/10.1155/2021/8836330.

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Stochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time or spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating nonlinear dependencies thus needs to be addressed based on the linear correlations. This paper first demonstrates the capture of nonlinear dependency by fitting various bivariate non-Gaussian copulas to limited data samples of structural material properties. The vine copula model is used to enable a flexible modeling of mu

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15

Segalas, Corentin, Catherine Helmer, and Hélène Jacqmin-Gadda. "A curvilinear bivariate random changepoint model to assess temporal order of markers." Statistical Methods in Medical Research 29, no. 9 (2020): 2481–92. http://dx.doi.org/10.1177/0962280219898719.

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In biomedical research, various longitudinal markers measuring different quantities are often collected over time. For example, repeated measures of psychometric scores are very informative about the degradation process toward dementia. These trajectories are generally nonlinear with an acceleration of the decline a few years before the diagnosis and a large heterogeneity between psychometric tests depending on the underlying cognitive function to be evaluated and the metrological properties of the test. Comparing the times of acceleration of the decline before diagnosis between cognitive test

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Jizba, Petr, Hynek Lavička, and Zlata Tabachová. "Causal Inference in Time Series in Terms of Rényi Transfer Entropy." Entropy 24, no. 7 (2022): 855. http://dx.doi.org/10.3390/e24070855.

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Uncovering causal interdependencies from observational data is one of the great challenges of a nonlinear time series analysis. In this paper, we discuss this topic with the help of an information-theoretic concept known as Rényi’s information measure. In particular, we tackle the directional information flow between bivariate time series in terms of Rényi’s transfer entropy. We show that by choosing Rényi’s parameter α, we can appropriately control information that is transferred only between selected parts of the underlying distributions. This, in turn, is a particularly potent tool for quan

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17

Shojaie, Ali, and Emily B. Fox. "Granger Causality: A Review and Recent Advances." Annual Review of Statistics and Its Application 9, no. 1 (2022): 289–319. http://dx.doi.org/10.1146/annurev-statistics-040120-010930.

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Introduced more than a half-century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience. Despite this popularity, the validity of this framework for inferring causal relationships among time series has remained the topic of continuous debate. Moreover, while the original definition was general, limitations in computational tools have constrained the applications of Granger causality to primarily simple bivariate vector autoregressive processes. Starting with a review of early develo

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18

Lasserre, Marvin, Régis Lebrun, and Pierre-Henri Wuillemin. "Learning Continuous High-Dimensional Models using Mutual Information and Copula Bayesian Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 12139–46. http://dx.doi.org/10.1609/aaai.v35i13.17441.

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We propose a new framework to learn non-parametric graphical models from continuous observational data. Our method is based on concepts from information theory in order to discover independences and causality between variables: the conditional and multivariate mutual information (such as \cite{verny2017learning} for discrete models). To estimate these quantities, we propose non-parametric estimators relying on the Bernstein copula and that are constructed by exploiting the relation between the mutual information and the copula entropy \cite{ma2011mutual, belalia2017testing}. To our knowledge,

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19

Morvan, Daniel, Jean-Louis Golmard, Michel Komajda, and Gérard Drobinski. "Model-based determination of cut-off values for left ventricular hypertrophy from echocardiographic myocardial mass data." Clinical Science 82, no. 1 (1992): 25–32. http://dx.doi.org/10.1042/cs0820025.

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1. The left ventricular myocardial mass is a measurement that is easy to obtain by echocardiography. It is currently used for the definition of left ventricular hypertrophy, but cut-off values are often critical, since they depend on covariates of left ventricular myocardial mass such as sex, age, body surface area, physical training, blood pressure, etc. As it is very difficult in any laboratory to obtain a sufficient number of normal subjects for the establishment of left ventricular myocardial mass experimental distributions, we propose a non-linear model for the calculation of echocardiogr

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20

Kumar, U., C. Milesi, R. R. Nemani, S. Kumar Raja, S. Ganguly, and W. Wang. "Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 59–65. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-59-2015.

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In this paper, we explore the possibility of sparse regression, a new direction in unmixing, for vegetation and urban area classification. SUnSAL (Sparse unmixing via variable splitting and augmented Lagrangian) in both unconstrained and constrained forms (with the abundance non-negativity and abundance sum-to-one constraints) were used with a set of global endmembers (substrate, vegetation and dark objects) to unmix a set of computer simulated noise-free and noisy data (with Gaussian noise of different signal-to-noise ratio) in order to judge the robustness of the algorithm. The error in the

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21

Prykhodko, S. B., A. V. Pukhalevych, K. S. Prykhodko, and L. M. Makarova. "NONLINEAR REGRESSION MODELS FOR ESTIMATING THE DURATION OF SOFTWARE DEVELOPMENT IN JAVA FOR PC BASED ON THE 2021 ISBSG DATA." Radio Electronics, Computer Science, Control, no. 3 (October 17, 2022): 144. http://dx.doi.org/10.15588/1607-3274-2022-3-14.

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Context. The problem of estimating the duration of software development in Java for personal computers (PC) is important because, first, failed duration estimating is often the main contributor to failed software projects, second, Java is a popular language, and, third, a personal computer is a widespread multi-purpose computer. The object of the study is the process of estimating the duration of software development in Java for PC. The subject of the study is the nonlinear regression models to estimate the duration of software development in Java for PC. Objective. The goal of the work

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Koohi Lai, Zahra, Ali Namaki, Ali Hosseiny, Gholamreza Jafari, and Marcel Ausloos. "Coupled Criticality Analysis of Inflation and Unemployment." Entropy 23, no. 1 (2020): 42. http://dx.doi.org/10.3390/e23010042.

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In this paper, we focus on the critical periods in the economy that are characterized by unusual and large fluctuations in macroeconomic indicators, like those measuring inflation and unemployment. We analyze U.S. data for 70 years from 1948 until 2018. To capture their fluctuation essence, we concentrate on the non-Gaussianity of their distributions. We investigate how the non-Gaussianity of these variables affects the coupling structure of them. We distinguish “regular” from “rare” events, in calculating the correlation coefficient, emphasizing that both cases might lead to a different respo

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Dell’Antonio, Larissa Soares, Franciéle Marabotti Costa Leite, Cristiano Soares da Silva Dell’Antonio, et al. "COVID-19 Mortality in Public Hospitals in a Brazilian State: An Analysis of the Three Waves of the Pandemic." International Journal of Environmental Research and Public Health 19, no. 21 (2022): 14077. http://dx.doi.org/10.3390/ijerph192114077.

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Objective: To analyze COVID-19 deaths in public hospitals in a Brazilian state, stratified by the three waves of the pandemic, and to test their association with socio-clinical variables. Methods: Observational analytical study, where 5436 deaths by COVID-19 occurred in hospitals of the public network of Espírito Santo, between 1 April 2020, and 31 August 2021, stratified by the three waves of the pandemic, were analyzed. For the bivariate analyses, the Pearson’s chi-square, Fisher’s Exact or Friedman’s tests were performed depending on the Gaussian or non-Gaussian distribution of the data. Fo

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24

Kuantama, Endrowednes, Radu Tarca, Simona Dzitac, Ioan Dzitac, Tiberiu Vesselenyi, and Ioan Tarca. "The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter." Sensors 19, no. 18 (2019): 3849. http://dx.doi.org/10.3390/s19183849.

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This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynam

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25

Ragno, Elisa, Markus Hrachowitz, and Oswaldo Morales-Nápoles. "Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges." Hydrology and Earth System Sciences 26, no. 6 (2022): 1695–711. http://dx.doi.org/10.5194/hess-26-1695-2022.

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Abstract. Non-parametric Bayesian networks (NPBNs) are graphical tools for statistical inference widely used for reliability analysis and risk assessment and present several advantages, such as the embedded uncertainty quantification and limited computational time for the inference process. However, their implementation in hydrological studies is still scarce. Hence, to increase our understanding of their applicability and extend their use in hydrology, we explore the potential of NPBNs to reproduce catchment-scale hydrological dynamics. Long-term data from 240 river catchments with contrastin

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Osmane, Adnane, Mikko Savola, Emilia Kilpua, Hannu Koskinen, Joseph E. Borovsky, and Milla Kalliokoski. "Quantifying the non-linear dependence of energetic electron fluxes in the Earth's radiation belts with radial diffusion drivers." Annales Geophysicae 40, no. 1 (2022): 37–53. http://dx.doi.org/10.5194/angeo-40-37-2022.

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Abstract. In this study, we use mutual information to characterise statistical dependencies of seed and relativistic electron fluxes in the Earth's radiation belts on ultra-low-frequency (ULF) wave power measured on the ground and at geostationary orbit. The benefit of mutual information, in comparison to measures such as the Pearson correlation, lies in the capacity to distinguish non-linear dependencies from linear ones. After reviewing the property of mutual information and its relationship with the Pearson correlation for Gaussian bivariates, we present a methodology to quantify and distin

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27

Anderson, Jeffrey L. "A Marginal Adjustment Rank Histogram Filter for Non-Gaussian Ensemble Data Assimilation." Monthly Weather Review, May 18, 2020. http://dx.doi.org/10.1175/mwr-d-19-0307.1.

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An extension to standard ensemble Kalman filter algorithms that can improve performance for non-Gaussian prior distributions, non-Gaussian likelihoods, and bounded state variables is described. The algorithm exploits the capability of the rank histogram filter (RHF) to represent arbitrary prior distributions for observed variables. The rank histogram algorithm can be applied directly to state variables to produce posterior marginal ensembles without the need for regression that is part of standard ensemble filters. These marginals are used to adjust the marginals obtained from a standard ensem

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28

Rathjens, Jonathan, Arthur Kolbe, Jürgen Hölzer, Katja Ickstadt, and Nadja Klein. "Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas." Statistics in Biosciences, October 27, 2023. http://dx.doi.org/10.1007/s12561-023-09396-4.

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AbstractWe analyze perinatal data including biometric and obstetric information as well as data on maternal smoking, among others. Birth weight is the primarily interesting response variable. Gestational age is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of birth weight and gestational age is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where the marginal distributions of birth weight and

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Nyoka, Raymond, Thomas N. O. Achia, Jimmy Omony, Samuel M. Musili, Anthony Gichangi, and Henry Mwambi. "Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya." BMC Public Health 19, no. 1 (2019). http://dx.doi.org/10.1186/s12889-019-7036-2.

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Anderson, Jeffrey L. "A Quantile-Conserving Ensemble Filter Framework. Part II: Regression of Observation Increments in a Probit and Probability Integral Transformed Space." Monthly Weather Review, July 14, 2023. http://dx.doi.org/10.1175/mwr-d-23-0065.1.

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Abstract Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian which is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and wate

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Grenier, Isabelle, Bruno Sansó, and Jessica L. Matthews. "Multivariate nearest‐neighbors Gaussian processes with random covariance matrices." Environmetrics, January 2, 2024. http://dx.doi.org/10.1002/env.2839.

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AbstractWe propose a non‐stationary spatial model based on a normal‐inverse‐Wishart framework, conditioning on a set of nearest‐neighbors. The model, called nearest‐neighbor Gaussian process with random covariance matrices is developed for both univariate and multivariate spatial settings and allows for fully flexible covariance structures that impose no stationarity or isotropic restrictions. In addition, the model can handle duplicate observations and missing data. We consider an approach based on integrating out the spatial random effects that allows fast inference for the model parameters.

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Baron, Ethan, Nathan Sandholtz, Devin Pleuler, and Timothy C. Y. Chan. "Miss it like Messi: Extracting value from off-target shots in soccer." Journal of Quantitative Analysis in Sports, January 1, 2024. http://dx.doi.org/10.1515/jqas-2022-0107.

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Abstract Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrounding soccer shots has given rise to model-based metrics which better cope with these challenges. Specifically, metrics such as expected goals added, goals above expectation, and post-shot expected goals all use advanced data to offer an improvement over the classical conversion rate. However, all metrics developed to date assign a value of zero to off-target shots, which account for almost two-thirds of all

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Gallus, C., E. M. Pothos, P. Blasiak, P. Blasiak, J. M. Yearsley, and B. W. Wojciechowski. "Bell correlations outside physics." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-31441-x.

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AbstractCorrelations are ubiquitous in nature and their principled study is of paramount importance in scientific development. The seminal contributions from John Bell offer a framework for analyzing the correlations between the components of quantum mechanical systems and have instigated an experimental tradition which has recently culminated with the Nobel Prize in Physics (2022). In physics, Bell’s framework allows the demonstration of the non-classical nature of quantum systems just from the analysis of the observed correlation patterns. Bell’s ideas need not be restricted to physics. Our

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