Výsledky vyhledávání - "Hamid Zareifard"

An empirical Bayes approach for learning directed acyclic graph using MCMC algorithm

Autor: Hamid Zareifard, Dariusz Plewczynski, Vahid Rezaei Tabar, Selva Salimi

Publikováno v: Statistical Analysis and Data Mining: The ASA Data Science Journal. 12:394-403

One hypothetically well‐founded approach for learning a Directed Acyclic Graph (DAG) is to utilize the Markov Chain Monte Carlo (MCMC) techniques. In the MCMC, the uniform noninformative p...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0c2f2cccd8dc040172549bb8cbbe0e0a
https://doi.org/10.1002/sam.11430

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Learning directed acyclic graphs by determination of candidate causes for discrete variables

Autor: Dariusz Plewczynski, Hamid Zareifard, Selva Salimi, Vahid Rezaei Tabar

Publikováno v: Journal of Statistical Computation and Simulation. 89:1957-1970

The aim of this paper is learning directed acyclic graph (DAG) by determination of candidate causes for each discrete variable. Based on the fact that the candidate causes of a variable must be a s...

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https://doi.org/10.1080/00949655.2019.1604709

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Multivariate spatial modelling through a convolution-based skewed process

Autor: Hamid Zareifard, Omar Dahdouh, Majid Jafari Khaledi

Publikováno v: Stochastic Environmental Research and Risk Assessment. 33:657-671

In some statistical issues, several continuous spatial outcomes are simultaneously measured at each sampling location. In such circumstances, it is common to model the data through a multivariate Gaussian model. As the normality assumption is often u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a517e73e9402e1f75d6efe3e7d2129e0
https://doi.org/10.1007/s00477-019-01657-3

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Finding a set of candidate parents using dependency criterion for the K2 algorithm

Autor: Selva Salimi, Vahid Rezaei Tabar, Farzad Eskandari, Hamid Zareifard

Publikováno v: Pattern Recognition Letters. 111:23-29

One of the most effective structure-learning methods in a Bayesian network is the K2 algorithm. Because the performance of the K2 algorithm depends on the node ordering, more effective node ordering inference methods are needed. In this paper, we int

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2ca2462aa9aa2e7889b82994d8641a20
https://doi.org/10.1016/j.patrec.2018.04.019

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A heterogeneous Bayesian regression model for skewed spatial data

Autor: Hamid Zareifard, Majid Jafari Khaledi

Publikováno v: Spatial Statistics. 46:100545

After displaying skewness, spatial data can cause nonstationarity. This paper develops a hierarchical skew-Gaussian process capable of simultaneously handling skewness and nonstationarity. At the first level of the hierarchy, we specify a multivariat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7d80330cf493b371b1f58e38fb6003c4
https://doi.org/10.1016/j.spasta.2021.100545

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Modeling Skewed Spatial Data Using a Convolution of Gaussian and Log-Gaussian Processes

Autor: Firoozeh Rivaz, Majid Jafari Khaledi, Mohammad Q. Vahidi-Asl, Hamid Zareifard

Publikováno v: Bayesian Anal. 13, no. 2 (2018), 531-557

In spatial statistics, it is usual to consider a Gaussian process for spatial latent variables. As the data often exhibit non-normality, we introduce a novel skew process, named hereafter Gaussian-log Gaussian convolution (GLGC) to construct latent s

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https://doi.org/10.1214/17-ba1064

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Empirical Bayes Estimation in Regression Models with Generalized Skew-Slash Errors

Autor: Majid Jafari Khaledi, Hamid Zareifard

Publikováno v: Communications in Statistics - Theory and Methods. 42:1105-1123

In this article, utilizing a scale mixture of skew-normal distribution in which mixing random variable is assumed to follow a mixture model with varying weights for each observation, we introduce a generalization of skew-normal linear regression mode

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https://doi.org/10.1080/03610926.2011.593282

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Non-Gaussian modeling of spatial data using scale mixing of a unified skew Gaussian process

Autor: Hamid Zareifard, Majid Jafari Khaledi

Publikováno v: Journal of Multivariate Analysis. 114:16-28

In this paper, we introduce a unified skew Gaussian-log Gaussian model and propose a general class of spatial sampling models that can account for both heavy tails and skewness. This class includes some models proposed previously in the literature. T

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https://doi.org/10.1016/j.jmva.2012.07.003

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A skew Gaussian decomposable graphical model

Autor: Håvard Rue, Finn Lindgren, Majid Jafari Khaledi, Hamid Zareifard

Publikováno v: Zareifard, H, Rue, H, Khaledi, M J & Lindgren, F 2016, ' A skew Gaussian decomposable graphical model ', Journal of Multivariate Analysis, vol. 145, pp. 58-72 . https://doi.org/10.1016/j.jmva.2015.08.011

This paper proposes a novel decomposable graphical model to accommodate skew Gaussian graphical models. We encode conditional independence structure among the components of the multivariate closed skew normal random vector by means of a decomposable

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