Special Issue "New Trends in Statistical Computing and Data Science"

Submission Deadline: 30 June 2022
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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
  • Time Synchronized Velocity Error for Trajectory Compression
  • 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
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  • A New Rayleigh Distribution: Properties and Estimation Based on Progressive Type-II Censored Data with an Application
  • 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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