منابع مشابه
Gaussian Copula Marginal Regression for Modeling Extreme Data with Application
Regression is commonly used to determine the relationship between the response variable and the predictor variable, where the parameters are estimated by Ordinary Least Square (OLS). This method can be used with an assumption that residuals are normally distributed (0, σ). However, the assumption of normality of the data is often violated due to extreme observations, which are often found in th...
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Model selection is an important activity in modern data analysis and the conventional Bayesian approach to this problem involves calculation of marginal likelihoods for different models, together with diagnostics which examine specific aspects of model fit. Calculating the marginal likelihood is a difficult computational problem. Our article proposes some extensions of the Laplace approximation...
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Quantitative studies in many fields involve the analysis of multivariate data of diverse types, including measurements that we may consider binary, ordinal and continuous. One approach to the analysis of such mixed data is to use a copula model, in which the associations among the variables are parameterized separately from their univariate marginal distributions. The purpose of this article is...
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The Mixed-Membership Stochastic Blockmodels (MMSB) is a popular framework for modelling social relationships by fully exploiting each individual node’s participation (or membership) in a social network. Despite its powerful representations, MMSB assumes that the membership indicators of each pair of nodes (i.e., people) are distributed independently. However, such an assumption often does not h...
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This report summarizes my contributions to copulas modeling. Two main research topics are addressed: The construction of semiparametric family of copulas based on a set of orthonormal functions and a matrix and the design of efficient estimation procedures.
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ژورنال
عنوان ژورنال: Journal of Business & Economic Statistics
سال: 2018
ISSN: 0735-0015,1537-2707
DOI: 10.1080/07350015.2018.1469998