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Symmetry
Special Issues
Statistical Modeling of Symmetry or Asymmetry Phenomena in Complex Data
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Statistical Modeling of Symmetry or Asymmetry Phenomena in Complex Data

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 5216

Special Issue Editors

Prof. Dr. Fukang Zhu

E-Mail Website
Guest Editor
School of Mathematics, Jilin University, Changchun, China
Interests: statistics
Prof. Dr. Shishun Zhao

E-Mail Website
Guest Editor
School of Mathematics, Jilin University, Changchun, China
Interests: statistics

Special Issue Information

Dear Colleagues,

The data in statistics textbooks are mostly idealized and simplistic, and the statistical methods listed in these books are only for this kind of data. Complex data include longitudinal data, microarray data, and time series data, among others, and they usually present symmetry or asymmetry phenomena. The complex structures of these data propose a variety of new challenges because classical theories and methodologies may fail to work. In the past few decades, much attention has been paid to developing new theories and methodologies for various kinds of complex data.

In this Special Issue, we are interested in research papers concerned with the following disciplines:

  • Bioinformation;
  • Biostatistics;
  • Time Series.

Papers including real applications, and those covering theorical aspects of statistical analysis and dealing with symmetry or asymmetry phenomena of the above three kinds of complex data, are particularly welcome. This Special Issue is also open to interdisciplinary research, comprehensive survey papers and computational software papers, with contributions to these disciplines.

Prof. Dr. Fukang Zhu
Prof. Dr. Shishun Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformation
  • biostatistics
  • complex data
  • statistical inference
  • statistical modeling
  • symmetry
  • time series

Published Papers (3 papers)

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Research

15 pages, 306 KiB  
Article
An Iteration Algorithm for American Options Pricing Based on Reinforcement Learning
by Nan Li
Symmetry 2022, 14(7), 1324; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14071324 - 27 Jun 2022
Cited by 3 | Viewed by 2108
Abstract
In this paper, we present an iteration algorithm for the pricing of American options based on reinforcement learning. At each iteration, the method approximates the expected discounted payoff of stopping times and produces those closer to optimal. In the convergence analysis, a finite [...] Read more.
In this paper, we present an iteration algorithm for the pricing of American options based on reinforcement learning. At each iteration, the method approximates the expected discounted payoff of stopping times and produces those closer to optimal. In the convergence analysis, a finite sample bound of the algorithm is derived. The algorithm is evaluated on a multi-dimensional Black-Scholes model and a symmetric stochastic volatility model, the numerical results implied that our algorithm is accurate and efficient for pricing high-dimensional American options. Full article
(This article belongs to the Special Issue Statistical Modeling of Symmetry or Asymmetry Phenomena in Complex Data)
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20 pages, 511 KiB  
Article
A New Bivariate Random Coefficient INAR(1) Model with Applications
by Qi Li, Huaping Chen and Xiufang Liu
Symmetry 2022, 14(1), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14010039 - 29 Dec 2021
Cited by 7 | Viewed by 1309
Abstract
Excess zeros is a common phenomenon in time series of counts, but it is not well studied in asymmetrically structured bivariate cases. To fill this gap, we first considered a new first-order, bivariate, random coefficient, integer-valued autoregressive model with a bivariate innovation, which [...] Read more.
Excess zeros is a common phenomenon in time series of counts, but it is not well studied in asymmetrically structured bivariate cases. To fill this gap, we first considered a new first-order, bivariate, random coefficient, integer-valued autoregressive model with a bivariate innovation, which follows the asymmetric Hermite distuibution with five parameters. An attractive advantage of the new model is that the dependence between series is achieved by innovative parts and the cross-dependence of the series. In addition, the time series of counts are modeled with excess zeros, low counts and low over-dispersion. Next, we established the stationarity and ergodicity of the new model and found its stochastic properties. We discuss the conditional maximum likelihood (CML) estimate and its asymptotic property. We assessed finite sample performances of estimators through a simulation study. Finally, we demonstrate the superiority of the proposed model by analyzing an artificial dataset and a real dataset. Full article
(This article belongs to the Special Issue Statistical Modeling of Symmetry or Asymmetry Phenomena in Complex Data)
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27 pages, 915 KiB  
Article
First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts
by Jie Zhang, Dehui Wang, Kai Yang and Xiaogang Dong
Symmetry 2021, 13(12), 2271; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122271 - 29 Nov 2021
Cited by 1 | Viewed by 1146
Abstract
In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted [...] Read more.
In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model. Full article
(This article belongs to the Special Issue Statistical Modeling of Symmetry or Asymmetry Phenomena in Complex Data)
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