Table of Content

New Trends in Statistical Computing and Data Science

Submission Deadline: 30 June 2022 (closed)

Guest Editors

Dr. Christophe Chesneau, Université de Caen, France
Dr. Hasssan Doosti, Macquarie University, Australia

Summary

The interface between computer science and statistics has developed considerably in recent years, with exponential progress in the fields of data analysis, stochastic modeling, machine learning, econometrics, simulation, algorithms, classification and networks. Innovative discoveries in this field appear every day, opening new scientific horizons to the modern world. This is particularly true in the post year 2020  with the treatment of the large volumes of data that feed the daily activity of big companies, and the development of artificial intelligence including advanced machine learning techniques, especially the so-called Deep Learning.

This special issue aims to publish the most significant articles in this direction, that is to say the current progress of statistical computing and data science. Novelty, high quality and importance are the triptych of the special issue.

The scope includes, but is not limited to, the following overlapping topics: Artificial intelligence; Big data; Classification; Computational statistics; Dimension reduction: Distribution theory; Econometrics; Inference; Machine learning; Networks; Simulation; Statistical algorithms; Stochastic modelling.



Published Papers


  • Open Access

    ARTICLE

    Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

    Zhiwen Xie, Tiantang Yu
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2119-2135, 2023, DOI:10.32604/cmes.2023.025070
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract Deformation can directly reflect the working behavior of the dam, so determining the deformation monitoring control value can effectively monitor the safety of dam operation. The traditional dam deformation monitoring control value only considers the single measuring point. In order to overcome the limitation, this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points. A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of each measuring point. The projection pursuit method… More >

  • Open Access

    ARTICLE

    A Novel Modified Alpha Power Transformed Weibull Distribution and Its Engineering Applications

    Refah Alotaibi, Hassan Okasha, Mazen Nassar, Ahmed Elshahhat
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2065-2089, 2023, DOI:10.32604/cmes.2023.023408
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract This paper suggests a new modified version of the traditional Weibull distribution by adding a new shape parameter utilising the modified alpha power transformed technique. We refer to the new model as modified alpha power transformed Weibull distribution. The attractiveness and significance of the new distribution lie in its power to model monotone and non-monotone failure rate functions, which are quite familiar in environmental investigations. Its hazard rate function can be decreasing, increasing, bathtub and upside-down then bathtub shaped. Diverse structural properties of the proposed model are acquired including quantile function, moments, entropies, order statistics, residual life and reversed failure… More >

  • Open Access

    ARTICLE

    Ranked-Set Sampling Based Distribution Free Control Chart with Application in CSTR Process

    Ibrahim M. Almanjahie, Zahid Rasheed, Majid Khan, Syed Masroor Anwar, Ammara Nawaz Cheema
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2091-2118, 2023, DOI:10.32604/cmes.2023.022201
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract Nonparametric (distribution-free) control charts have been introduced in recent years when quality characteristics do not follow a specific distribution. When the sample selection is prohibitively expensive, we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts. In this study, we proposed a nonparametric homogeneously weighted moving average based on the Wilcoxon signed-rank test with ranked set sampling () control chart for detecting shifts in the process location of a continuous and symmetric distribution. Monte Carlo simulations are used to obtain the run length characteristics to evaluate the performance of the… More >

  • Open Access

    ARTICLE

    A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data: An Application to COVID-19

    Behnaz Alafchi, Leili Tapak, Hassan Doosti, Christophe Chesneau, Ghodratollah Roshanaei
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2047-2064, 2023, DOI:10.32604/cmes.2022.022647
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract Survival data with a multi-state structure are frequently observed in follow-up studies. An analytic approach based on a multi-state model (MSM) should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events. One main objective in the MSM framework is variable selection, where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression. The usual variable selection methods, including stepwise and penalized methods, do not provide information about the importance of variables. In this context, we present a two-step algorithm to evaluate the importance… More >

  • Open Access

    ARTICLE

    A New Three-Parameter Inverse Weibull Distribution with Medical and Engineering Applications

    Refah Alotaibi, Hassan Okasha, Hoda Rezk, Mazen Nassar
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1255-1274, 2023, DOI:10.32604/cmes.2022.022623
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract The objective of this article is to provide a novel extension of the conventional inverse Weibull distribution that adds an extra shape parameter to increase its flexibility. This addition is beneficial in a variety of fields, including reliability, economics, engineering, biomedical science, biological research, environmental studies, and finance. For modeling real data, several expanded classes of distributions have been established. The modified alpha power transformed approach is used to implement the new model. The data matches the new inverse Weibull distribution better than the inverse Weibull distribution and several other competing models. It appears to be a distribution designed to… More >

  • Open Access

    ARTICLE

    Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

    Sunisa Junnumtuam, Sa-Aat Niwitpong, Suparat Niwitpong
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1229-1254, 2023, DOI:10.32604/cmes.2022.022098
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) distribution is proposed for the first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of the new distribution, such as the moment generating function, mean, and variance are presented. Furthermore, confidence intervals are constructed by using the Wald, Bayesian, and highest posterior density (HPD) methods to estimate the true confidence intervals for the parameters of the ZICG distribution. Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the… More >

    Graphic Abstract

    Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

  • Open Access

    ARTICLE

    New Hybrid EWMA Charts for Efficient Process Dispersion Monitoring with Application in Automobile Industry

    Xuechen Liu, Majid Khan, Zahid Rasheed, Syed Masroor Anwar, Muhammad Arslan
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 1171-1195, 2022, DOI:10.32604/cmes.2022.019199
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract The EWMA charts are the well-known memory-type charts used for monitoring the small-to-intermediate shifts in the process parameters (location and/or dispersion). The hybrid EWMA (HEWMA) charts are enhanced version of the EWMA charts, which effectively monitor the process parameters. This paper aims to develop two new uppersided HEWMA charts for monitoring shifts in process variance, i.e., HEWMA1 and HEWMA2 charts. The design structures of the proposed HEWMA1 and HEWMA2 charts are based on the concept of integrating the features of two EWMA charts. The HEWMA1 and HEWMA2 charts plotting statistics are developed using one EWMA statistic as input for the… More >

  • Open Access

    ARTICLE

    Improving Date Fruit Classification Using CycleGAN-Generated Dataset

    Dina M. Ibrahim, Nada M. Elshennawy
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN) models and avoid the overfitting… More >

  • Open Access

    ARTICLE

    Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

    W. A. Shaikh, S. F. Shah, S. M. Pandhiani, M. A. Solangi
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1517-1532, 2022, DOI:10.32604/cmes.2022.017822
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract This investigative study is focused on the impact of wavelet on traditional forecasting time-series models, which significantly shows the usage of wavelet algorithms. Wavelet Decomposition (WD) algorithm has been combined with various traditional forecasting time-series models, such as Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS) and their effects are examined in terms of the statistical estimations. The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters, which has yielded tremendous constructive outcomes. Further, it is observed that the wavelet combined models are classy… More >

  • Open Access

    ARTICLE

    Time Synchronized Velocity Error for Trajectory Compression

    Haibao Jiang, Dezhi Han, Han Liu, Jiuzhang Han and Wenjing Nie
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1193-1219, 2022, DOI:10.32604/cmes.2022.017663
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract Nowadays, distance is usually used to evaluate the error of trajectory compression. These methods can effectively indicate the level of geometric similarity between the compressed and the raw trajectory, but it ignores the velocity error in the compression. To fill the gap of these methods, assuming the velocity changes linearly, a mathematical model called SVE (Time Synchronized Velocity Error) for evaluating compression error is designed, which can evaluate the velocity error effectively, conveniently and accurately. Based on this model, an innovative algorithm called SW-MSVE (Minimum Time Synchronized Velocity Error Based on Sliding Window) is proposed, which can minimize the velocity… More >

  • Open Access

    ARTICLE

    A New Rayleigh Distribution: Properties and Estimation Based on Progressive Type-II Censored Data with an Application

    Ali Algarni, Abdullah M. Almarashi
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 379-396, 2022, DOI:10.32604/cmes.2022.017714
    (This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)
    Abstract In this paper, we propose a new extension of the traditional Rayleigh distribution called the modified Kies Rayleigh distribution. The new distribution contains one scale and one shape parameter and its hazard rate function can be increasing and bathtub-shaped. Some mathematical properties of the new distribution are derived including quantiles and moments. The parameters of modified Kies Rayleigh distribution are estimated based on progressively Type-II censored data. For this purpose, we consider two estimation methods, namely maximum likelihood and maximum product of spacing estimation methods. To compare the efficiency of the proposed estimators, a simulation study is carried out. To… More >

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