Hossein Haghbin

Hossein Haghbin
Persian Gulf University | PGU · Department of Statistics

Ph.d of Statistics

About

24
Publications
3,067
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188
Citations
Citations since 2017
16 Research Items
183 Citations
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201720182019202020212022202301020304050
201720182019202020212022202301020304050

Publications

Publications (24)
Chapter
In this chapter, we develop multivariate functional singular spectrum analysis (MFSSA) over different dimensional domains with the goal of decomposing a multivariate functional time series (MFTS) into interpretable partitions such as mean, periodic, and trend components. The approach is flexible in the sense that the MFTS signal may be composed of...
Article
Full-text available
In this paper, a new class of the continuous distributions is established via compounding the arctangent function with generalized log-logistic class of distributions. Some structural properties of the suggested model such as distribution function, hazard function, quantile function, asymptotics and a useful expansion for the new class are given in...
Article
Purpose: Coronary calcium scores (CCSs) in cardiac-gated computed tomography (CCT) are diagnostic for coronary artery disease (CAD). This study aims to investigate if CCSs can foretell CAD-reporting and data system (CAD-RADS) without performing computed tomography angiography (CTA). Methods: Profiles of 544 patients were studied who had gone thr...
Article
Full-text available
In recent years large datasets of lexical processing times have been released for several languages, including English, French, Spanish, and Dutch. Such datasets have enabled us to study, compare, and model the global effects of many psycholinguistic measures such as word frequency, orthographic neighborhood (ON) size, and word length. We have comp...
Article
Full-text available
Functional autoregressive models are popular for functional time series analysis, but the standard formulation fails to address seasonal behaviour in functional time series data. To overcome this shortcoming, we introduce seasonal functional autoregressive time series models. For the model of order one, we derive sufficient stationarity conditions...
Preprint
Full-text available
In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict future data points where recu...
Article
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In this paper, we develop a new extension of the Singular Spectrum Analysis (SSA) called functional SSA to analyze functional time series. The new methodology is constructed by integrating ideas from functional data analysis and univariate SSA. Specifically, we introduce a trajectory operator in the functional world, which is equivalent to the traj...
Preprint
Full-text available
In this work, we develop multivariate functional singular spectrum analysis (MFSSA) over different dimensional domains which is the functional extension of multivariate singular spectrum analysis (MSSA). In the following, we provide all of the necessary theoretical details supporting the work as well as the implementation strategy that contains the...
Preprint
Full-text available
In this paper, we introduce a new extension of the Singular Spectrum Analysis (SSA) called functional SSA to analyze functional time series. The new methodology is developed by integrating ideas from functional data analysis and univariate SSA. We explore the advantages of the functional SSA in terms of simulation results and with an application to...
Article
Functional time series is a popular method of forecasting in functional data analysis. The Box-Jenkins methodology for model building, with the aim of forecasting, includes three iterative steps of model identification, parameter estimation and diagnostic checking. Portmanteau tests are one of the most popular diagnostic checking tools. In particul...
Article
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Based on the generalized log-logistic family (Gleaton and Lynch (2006)) of distributions, we propose a new family of continuous distributions with two extra shape parameters called the exponentiated odd log-logistic family. It extends the class of exponentiated distributions, odd log-logistic family (Gleaton and Lynch (2006)) and any continuous dis...
Article
Autoregressive Hilbertian (ARH) processes are of great importance in the analysis of functional time series data and estimation of the autocorrelation operators attracts the attention of various researchers. In this paper, we study estimators of the autocorrelation operators of periodically correlated autoregressive Hilbertian processes of order on...
Article
Full-text available
A comparative study based on the structure-property regression analysis is performed in order to test and evaluate the application possibilities of various graph irregularity indices for the prediction of physico-chemical properties of octane isomers. By restricting attention to single-variable linear regressions, we investigate the stochastic rela...
Article
We introduce and study general mathematical properties of a new generator of continuous distributions with three extra parameters called the new generalized odd log-logistic family of distributions. The proposed family contains several important classes discussed in the literature as sub-models such as the proportional reversed hazard rate and odd...
Article
This paper is devoted to a study on the structure of tensorial products of periodically correlated autoregressive (PCAR) processes with values in separable Hilbert spaces. It will be demonstrated that the resulting processes are PCAR with values in the space of Hilbert-Schmidt operators. These processes are applied while studying the convergence ra...
Article
Infinite dimensional periodically correlated (PC) random fields are studied in spectral domain. A spectral characterization is given and harmonizability is established. The covariance operator is characterized where it is observed that an infinite dimensional PC field is a two-dimensional Fourier transform of a spectral random measure. Also, an evo...
Article
In this article, we consider Hilbertian spatial periodically correlated autoregressive models. Such a spatial model assumes periodicity in its autocorrelation function. Plausibly, it explains spatial functional data resulted from phenomena with periodic structures, as geological, atmospheric, meteorological and oceanographic data. Our studies on th...
Article
This article applies the EM-based (ECM and ECME) algorithms to find the maximum likelihood estimates of model parameters in general AR models with independent scaled t-distributed innovations whenever the degrees of freedom are unknown. The ECME, sharing advantages with both EM and Newton–Raphson algorithms, is an extension of ECM, which itself is...
Article
Full-text available
In this paper, we first introduce a new family of distributions, called Minimax Normal distributions, by using Kumaraswamy distribution. Then we use this family to find a generalization of the Balakrishnan skew-normal distribution by the name of skew minimax Normal distribution and we study some of its properties.
Article
The asymptotic distribution for the ratio of sample proportions in two independent bernoulli populations is introduced. The presented method can be used to derive the asymptotic confidence interval and hypothesis testing for the ratio of population proportions. The performance of the new interval is comparable with similar confi- dence intervals in...

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Projects (3)
Project
ISEDS at Persian Gulf University (PGU) has pioneered many of the tools and ideas behind the research and applications often classified as "intelligent systems" and “data science,” where computer science, electrical engineering, statistics, and mathematics join together. This Faculty sees an even brighter future for data science as it harnesses a wider set of ideas to build a new more subtle and powerful science of data. As well as being interested in prediction and statistical computation, our Faculty puts equal weight on designing experiments, modeling sophisticated dependencies (networks, data streams), and trying to understand and quantify causal mechanisms, not simply averages and associations, with large data sets. These views are reflected in our curriculum targeted to data science specialists, our faculty’s research, and the work of our research students.
Project
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data. Data Science Data mining OTG SNA