|
|
|
 |
Search published articles |
 |
|
Showing 6 results for Karimi
Dr Fatemeh Hosseini, Dr Omid Karimi, Ms Ahdiyeh Azizi,
Volume 23, Issue 1 (9-2018)
Abstract
Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study. One of the most important issues in the analysis of survival data with spatial dependence, is estimation of the parameters and prediction of the unknown values in known sites based on observations vector. In this paper to analyze this type of survival, Cox regression model with piecewise exponential function used as a hazard and spatial dependence as a Gaussian random field and as a latent variable is added to the model. Because there is no closed form for posterior distribution and full conditional distributions, also long computing for Markov chain Monte Carlo algorithms, to analyze the model are used the approximate Bayesian methods.
A practical example of how to implement an approximate Bayesian approach is presented.
Dr Fatemeh Hosseini, Dr Omid Karimi, Miss Fatemeh Hamedi,
Volume 24, Issue 1 (9-2019)
Abstract
Tree models represent a new and innovative way of analyzing large data sets by dividing predictor space into simpler areas. Bayesian Additive Regression Trees model, a model that we explain in this article, uses a totality of trees in its structure, since the combination of several trees from a tree only has a higher accuracy.
Then, this model is a tree-based model and a nonparametric model that uses general aggregation methods, and boosting algorithms in particular and in fact is extension of the classification and Regression Tree methods in which the decision tree exists in the structure of these methods.
In this method, on the parameters of the model sum of tree and put regular prior then use the boosting algorithms for analysis. In this paper, first the Bayesian Additive Regression Trees model is introduced and then applied in survival analysis of lung cancer patients.
Omid Karimi, Fatemeh Hosseini,
Volume 25, Issue 1 (1-2021)
Abstract
Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on Poisson (Poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models is complex as analytic and so computation. The Bayesian approach using Monte Carlo Markov chain algorithms can be a solution to fit these models, although there are usually problems with low sample acceptance rates and long runtime to implement the algorithms. An appropriate solution is to use the Hamilton (hybrid) Monte Carlo algorithm
in The Bayesian approach. In this paper, the new Hamilton (hybrid) Monte Carlo method for Bayesian analysis of spatial count models on air pollution data in Tehran is studied. Also, the two common Monte Carlo algorithms such as the Markov chain (Gibbs and Metropolis-Hastings) and Langevin-Hastings are used to apply the complete Bayesian approach to the data modeling. Finally, an appropriate approach to data analysis and forecasting in all points of the city is introduced with the diagnostic criteria.
Fatemeh Hossini, Omid Karimi,
Volume 25, Issue 1 (1-2021)
Abstract
In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables, the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two new algorithms for the maximum likelihood estimations of parameters and to compare them in terms of speed and accuracy with existing algorithms. The presented algorithms are applied to a simulation study and their performances are compared.
Dr Fatemeh Hosseini, Dr Omid Karimi,
Volume 26, Issue 1 (12-2021)
Abstract
Spatial generalized linear mixed models are used commonly for modeling discrete spatial responses. In this models the spatial correlation of the data is considered as spatial latent variables. For simplicity, it is usually assumed in these models that spatial latent variables are normally distributed. An incorrect normality assumption may leads to inaccurate results and is therefore erroneous. In this paper we model the spaial latent variables in a general random field, namely the closed skew Gaussian random field which is more flexible and includes the Gaussian random field. We propose a new algorithm for maximum likelihood estimates of the parameters. A key ingredient in our algorithm is using a Hamiltonian Monte Carlo version of the EM algorithm. The performance of the proposed model and algorithm is presented through a simulation study.
Dr Fatemeh Hosseini, Dr Omid Karimi,
Volume 27, Issue 1 (3-2023)
Abstract
Spatial generalized linear mixed model is commonly used to model Non-Gaussian data and the spatial correlation of the data is modelled by latent variables. In this paper, latent variables are modeled using a stationary skew Gaussian random field and a new algorithm based on composite marginal likelihood is presented. The performance of this stationary random field in the model and the proposed algorithm is implemented in a simulation example.
|
|
