Symmetry Applied in Bayes and Statistics

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

Deadline for manuscript submissions: closed (16 January 2023) | Viewed by 1356

Special Issue Editor


E-Mail Website
Guest Editor
Division of Biostatistics, Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University No. 155 Linong Street Sec 2. Beitou District, Taipei 112, Taiwan
Interests: artificial neural networks; big data analytics; biostatistics; biomedical statistica model; statistical genetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We sincerely invite manuscripts with novel statistical research and data science approaches, such as Bayesian, regression models, survival analysis, statistical genetics, machine learning, and neural networks. This Special Issue specifically welcomes theoretical developments and applied research in all research fields related to symmetry. We are looking for methodological work related to statistics and biostatistics. Inferential work for statistics and data sciences will be highly appreciated. In particular, descriptive analytics, diagnostic tools, prognostic models, predictive models, machine learning algorithms, and prescriptive approaches are all desired. Applied research and discoveries in medicine, public health, and environmental research are encouraged for this Special Issue of Symmetry.

Dr. Chao-Yu Guo
Guest Editor

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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 3231 KiB  
Article
Bayesian P-Splines Quantile Regression of Partially Linear Varying Coefficient Spatial Autoregressive Models
by Zhiyong Chen, Minghui Chen and Fangyu Ju
Symmetry 2022, 14(6), 1175; https://doi.org/10.3390/sym14061175 - 7 Jun 2022
Cited by 2 | Viewed by 1820
Abstract
This paper deals with spatial data that can be modelled by partially linear varying coefficient spatial autoregressive models with Bayesian P-splines quantile regression. We evaluate the linear and nonlinear effects of covariates on the response and use quantile regression to present comprehensive information [...] Read more.
This paper deals with spatial data that can be modelled by partially linear varying coefficient spatial autoregressive models with Bayesian P-splines quantile regression. We evaluate the linear and nonlinear effects of covariates on the response and use quantile regression to present comprehensive information at different quantiles. We not only propose an empirical Bayesian approach of quantile regression using the asymmetric Laplace error distribution and employ P-splines to approximate nonparametric components but also develop an efficient Markov chain Monte Carlo technique to explore the joint posterior distributions of unknown parameters. Monte Carlo simulations show that our estimators not only have robustness for different spatial weight matrices but also perform better compared with quantile regression and instrumental variable quantile regression estimators in finite samples at different quantiles. Finally, a set of Sydney real estate data applications is analysed to illustrate the performance of the proposed method. Full article
(This article belongs to the Special Issue Symmetry Applied in Bayes and Statistics)
Show Figures

Figure 1

Back to TopTop