Browsing by Author "Maleki, Mohsen"

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A Bayesian Approach to Heavy-Tailed Finite Mixture Autoregressive Models
(2020) Baleanu, Dumitru; Maleki, Mohsen; Baleanu, Dumitru; Nguye, Vu-Thanh; Pho, Kim-Hung; 56389
In this paper, a Bayesian analysis of finite mixture autoregressive (MAR) models based on the assumption of scale mixtures of skew-normal (SMSN) innovations (called SMSN-MAR) is considered. This model is not simultaneously sensitive to outliers, as the celebrated SMSN distributions, because the proposed MAR model covers the lightly/heavily-tailed symmetric and asymmetric innovations. This model allows us to have robust inferences on some non-linear time series with skewness and heavy tails. Classical inferences about the mixture models have some problematic issues that can be solved using Bayesian approaches. The stochastic representation of the SMSN family allows us to develop a Bayesian analysis considering the informative prior distributions in the proposed model. Some simulations and real data are also presented to illustrate the usefulness of the proposed models.
On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors
(MDPI, 2019) Baleanu, Dumitru; Mahmoudi, Mohammad Reza; Baleanu, Dumitru; Maleki, Mohsen; 56389
In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.
On comparing and clustering the spectral densities of several almost cyclostationary processes
(2020) Baleanu, Dumitru; Maleki, Mohsen; Borodin, Kirill; Pho, Kim-Hung; Baleanu, Dumitru; 56389
In time series analysis, comparing spectral densities of several processes with almost peri-odic spectra is an interested problem. The contribution of this work is to give a technique to com-pare and to cluster the spectral densities of some independent almost periodically correlated (cyclostationary) processes. This approach is based on the limiting distribution for the periodogram and the discrete Fourier transform. The real world examples and simulation results indicate that the approach well acts. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).