En
  • دکتری (1384)

    آمار

    دانشگاه تربیت مدرس،

  • کارشناسی‌ارشد (1379)

    آمار، آمار ریاضی

    دانشگاه تربیت مدرس،

  • تحلیل داده های فضایی-زمانی
  • استنباط بیزی

    دارای دکتری تخصصی در آمار فضایی از دانشگاه تربیت مدرس، مهارت در نظریه احتمال، آمار بیزی، تحلیل داده‌های فضایی و فضایی-زمانی، مدل‌سازی آماری

    ارتباط

    رزومه

    Spatio‐temporal analysis of misaligned burden of disease data using a geo‐statistical approach

    Mahboubeh Parsaeian, Majid Jafari Khaledi, Farshad Farzadfar, Mahdi Mahdavi, Hojjat Zeraati, Mahmood Mahmoudi, Ardeshir Khosravi, Kazem Mohammad
    Journal PapersStatistics in Medicine , Volume 40 , Issue 4, 2021 February 20, {Pages 1021-1033 }

    Abstract

    Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo‐statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We appli

    A heterogeneous Bayesian regression model for skewed spatial data

    H Zareifard, MJ Khaledi
    Journal Papers , , {Pages }

    Abstract

    Spatio-temporal prediction of the population size at Tehran urban areas using BirnbaumSaunders Markov random fields

    MJ Khaledi, S Bourbour, H Safarkhanloo
    Journal Papers , , {Pages }

    Abstract

    Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model

    A Saffar, V Malekian, MJ Khaledi, Y Mehrabi
    Journal Papers , , {Pages }

    Abstract

    The polar-generalized normal distribution: properties, Bayesian estimation and applications

    M Faridi, MJ Khaledi
    Journal Papers , , {Pages }

    Abstract

    Generalized spatial stick-breaking processes

    Omar Dahdouh, Majid Jafari Khaledi
    Journal PapersCommunications in Statistics-Simulation and Computation , 2020 March 31, {Pages 20-Jan }

    Abstract

    This paper develops a Bayesian nonparametric model for skewed spatial data with nonstationary dependence structure. A transformed Gaussian model is proposed for the atoms of the kernel stick-breaking process by transforming the margins of a Gaussian process to flexible marginal distributions. This study proves that the correlation structure of the underlying spatial process is nonstationary. Results from both simulated and real datasets demonstrate that the proposed model possesses better spatial prediction performance and offers computational advantages compared to the Bayesian nonparametric model with the Gaussian base measure.

    Estimation of coppice forest characteristics using spatial and non-spatial models and Landsat data

    Somayeh Izadi, Hormoz Sohrabi, Majid Jafari Khaledi
    Journal Papers , 2020 February 28, {Pages 14-Jan }

    Abstract

    Accurate spatial modelling of forest characteristics is one of the most important challenges in remote sensing applications. In this study, we compared the ability of Multiple Linear Regression (MLR), Geographically weighted regression (GWR), and Random Forest (RF) to estimate different forest attributes based on field sample data and Landsat 8 image. CA was modelled with the highest accuracy compared to other variables using GWR. GWR outperformed other methods. The highest and the lowest values of RMSE were for BA using RF (31.0%) and CA using GWR (12.0%), respectively.

    The polar-generalized normal distribution

    Masoud Faridi, Majid Jafari Khaledi
    Journal PapersarXiv preprint arXiv:2008.11765 , 2020 August 26, {Pages }

    Abstract

    This paper introduces an extension to the normal distribution through the polar method to capture bimodality and asymmetry, which are often observed characteristics of empirical data. The later two features are entirely controlled by a separate scalar parameter. Explicit expressions for the cumulative distribution function, the density function and the moments were derived. The stochastic representation of the distribution facilitates implementing Bayesian estimation via the Markov chain Monte Carlo methods. Some real-life data as well as simulated data are analyzed to illustrate the flexibility of the distribution for modeling asymmetric bimodality.

    Multivariate spatial modelling through a convolution-based skewed process

    Hamid Zareifard, Majid Jafari Khaledi, Omar Dahdouh
    Journal PapersStochastic Environmental Research and Risk Assessment , Volume 33 , Issue 3, 2019 March 1, {Pages 657-671 }

    Abstract

    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 untenable, this paper proposes a multivariate skewed spatial model which, by virtue of its capacity for capturing skewness, is potentially more flexible than symmetric ones. Specifically, a multivariate version of the Gaussian-log Gaussian convolution process is developed. The resulting covariance for the multivariate process is in general nonseparable. We also discuss the other properties of the induced covariance function. Furthermore, Markov chain Monte Carlo methods are used to m

    A Skew-Gaussian Spatio-Temporal Process with Non-Stationary Correlation Structure

    Zahra Barzegar, Firoozeh Rivaz, Jafari Khaledi
    Journal PapersJournal of The Iranian Statistical Society , Volume 18 , Issue 2, 2019 December 10, {Pages 63-85 }

    Abstract

    This paper develops a new class of spatio-temporal process models that can simultaneously capture skewness and non-stationarity. The proposed approach which is based on using the closed skew-normal distribution in the low-rank representation of stochastic processes, has several favorable properties. In particular, it greatly reduces the dimension of the spatio-temporal latent variables and induces flexible correlation structures. Bayesian analysis of the model is implemented through a Gibbs MCMC algorithm which incorporates a version of the Kalman filtering algorithm. All fully conditional posterior distributions have closed forms which show another advantageous property of the proposed model. We demonstrate the efficiency of our model thro

    Multivariate spatial modelling through a convolution-based skewed process.

    H Zareifard, M Jafari Khaledi, O Dahdouh
    Journal Papers , , {Pages }

    Abstract

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

    Hamid Zareifard, Majid Jafari Khaledi, Firoozeh Rivaz, Mohammad Q Vahidi-Asl
    Journal PapersBayesian Analysis , Volume 13 , Issue 2, 2018 January , {Pages 531-557 }

    Abstract

    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 spatial models which provide great flexibility in capturing skewness. Some properties including closed-form expressions for the moments and the skewness of the GLGC process are derived. Particularly, we show that the mean square continuity and differentiability of the GLGC process are established by those of the Gaussian and log-Gaussian processes considered in its structure. Moreover, the usefulness of the proposed approach is demonstrated through the analysis of spatial data, including mixed or

    The effects of soil clay content, surface rock fragments and their interactions on runoff and sediment yield during rainfall simulation

    Mehdi Bashiri, Hamid Reza Moradi, Mir Masoud Kheirkhah, Majid Jafari-Khaledid
    Journal PapersDesert Ecosystem Engineering Journal , Volume 1 , Issue 2, 2018 September 1, {Pages 67-79 }

    Abstract

    In the present research, the effects of surface rock fragments and soil clay content on surface runoff and soil loss was investigated under the laboratory conditions. The aim of the test was to increase the general understanding of how soil clay content and surface rock fragments affect the soil erosion process. A rainfall simulator was added to an erosion plot and these apparatuses were used to investigate the effects of varying soil clay content (SCC) and soil rock fragments (SRF) on soil erosion by measuring runoff volume and sediment yield at regular time intervals during the simulation. The results indicated that the main effects of soil clay content and surface rock fragments were all significant at the 0.95 level (p<0.05) for the run

    Invited Review Paper

    YB Wang, MH Chen, L Kuo, PO Lewis, DA Henderson, LJ Kirrane, ...
    Journal Papers , , {Pages }

    Abstract

    Monte Carlo approximation of likelihood function in spatial GLMMs through an empirical Bayes method

    Firoozeh Rivaz, Majid Jafari Khaledi
    Journal PapersCommunications in Statistics-Simulation and Computation , Volume 46 , Issue 2, 2017 February 7, {Pages 1322-1335 }

    Abstract

    In spatial generalized linear mixed models (SGLMMs), statistical inference encounters problems, since random effects in the model imply high-dimensional integrals to calculate the marginal likelihood function. In this article, we temporarily treat parameters as random variables and express the marginal likelihood function as a posterior expectation. Hence, the marginal likelihood function is approximated using the obtained samples from the posterior density of the latent variables and parameters given the data. However, in this setting, misspecification of prior distribution of correlation function parameter and problems associated with convergence of Markov chain Monte Carlo (MCMC) methods could have an unpleasant influence on the likeliho

    Likelihood Inference in A Multivariate Spatial GLMM with Skew Gaussian Random Effects Using the Slice-SAB Algorithm

    Firoozeh Rivaz, Majid Jafari Khaledi
    Journal PapersJournal of Statistical Theory and Applications , Volume 16 , Issue 1, 2017 February 28, {Pages 108-116 }

    Abstract

    This paper introduces a multivariate skew Gaussian process and uses it to extend the family of multivariate spatial generalized linear mixed models to include skew Gaussian random effects. In this setting, the param-eter estimation encounters problems because the likelihood function involves high dimensional integrations which are computationally intensive. For estimating parameters of the complicated model structure, this article proposes an algorithm which is a combination of boosting with a variant of stochastic approximation. This algorithm which known as stochastic approximation boosting (SAB) algorithm, uses the Markov chain Monte Carlo method based on slice sampling to obtain simulations from full conditional distribution of random e

    Supplementary Material of “Modeling Skewed Spatial Data Using a Convolution of Gaussian and Log-Gaussian Processes”

    H Zareifard, MJ Khaledi, F Rivaz, MQ Vahidi-Asl
    Journal Papers , , {Pages }

    Abstract

    A non-homogeneous skew-Gaussian Bayesian spatial model

    Hossein Boojari, Majid Jafari Khaledi, Firoozeh Rivaz
    Journal PapersStatistical Methods & Applications , Volume 25 , Issue 1, 2016 March 1, {Pages 55-73 }

    Abstract

    In spatial statistics, models are often constructed based on some common, but possible restrictive assumptions for the underlying spatial process, including Gaussianity as well as stationarity and isotropy. However, these assumptions are frequently violated in applied problems. In order to simultaneously handle skewness and non-homogeneity (i.e., non-stationarity and anisotropy), we develop the fixed rank kriging model through the use of skew-normal distribution for its non-spatial latent variables. Our approach to spatial modeling is easy to implement and also provides a great flexibility in adjusting to skewed and large datasets with heterogeneous correlation structures. We adopt a Bayesian framework for our analysis, and d

    An area-specific stick breaking process for spatial data

    Mahdi Hosseinpouri, Majid Jafari Khaledi
    Journal PapersStatistical Papers , 2016 January , {Pages 23-Jan }

    Abstract

    Most of the existing Bayesian nonparametric models for spatial areal data assume that the neighborhood structures are known, however in practice this assumption may not hold. In this paper, we develop an area-specific stick breaking process for distributions of random effects with the spatially-dependent weights arising from the block averaging of underlying continuous surfaces. We show that this prior, which does not depend on specifying neighboring schemes, is noticeably flexible in effectively capturing heterogeneity in spatial dependency across areas. We illustrate the methodology with a dataset involving expenditure credit of 31 provinces of Iran.

    A skew Gaussian decomposable graphical model

    Hamid Zareifard, H?vard Rue, Majid Jafari Khaledi, Finn Lindgren
    Journal PapersJournal of Multivariate Analysis , Volume 145 , 2016 March 1, {Pages 58-72 }

    Abstract

    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 graph so that the pattern of zero off-diagonal elements in the precision matrix corresponds to the missing edges of the given graph. We present conditions that guarantee the propriety of the posterior distributions under the standard noninformative priors for mean vector and precision matrix, and a proper prior for skewness parameter. The identifiability of the parameters is investigated by a simulation study. Finally, we apply our methodology to two data sets.

    دروس نیمسال جاری

    • كارشناسي ارشد
      نظريه اندازه و احتمال2 ( واحد)
      دانشکده علوم ریاضی، گروه آمار كاربردي
    • كارشناسي ارشد
      يادگيري آماري ( واحد)

    دروس نیمسال قبل

    • كارشناسي ارشد
      نظريه اندازه و احتمال 1 ( واحد)
      دانشکده علوم ریاضی، گروه آمار كاربردي
    • كارشناسي ارشد
      نظريه اندازه و احتمال2 ( واحد)
    • 1398
      ميرزاوند, حسن
      توسعه دو مدي مدل هاي فضايي چوله گاوسي
    • 1397
      فريدي, مسعود
      توزيع‌ بتا-گاوسي تعميم يافته با كاربرد در مدل‌‌سازي داده‌هاي فضايي
      داده ای یافت نشد
      داده ای یافت نشد

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