Statistics: Theories and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 884
Special Issue Editors
Interests: Bayesian statistics; financial econometrics; interval estimation and hypothesis testing; quality control; quantile regression; statistical learning with big data; survival analysis; regression diagnostics; time series analysis
Interests: statistical methods for pairwise distance-based analysis and applications; statistical and computational methods for the analysis of genetics/genomics data, including investigations of gene–gene/gene–environment interactions; genome-wide association studies, systems biology, and epigenome-wide association studies; statistical design for health studies; identification of cell-specific transcriptional variations associated with human diseases
Interests: Bayesian statistics; empirical likelihood; nonparametric inference; high-dimensional data analysis; longitudinal data analysis; quantile regression; structural equation models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are excited to introduce a Special Issue focusing on “Statistics: Theories and Applications.” Today, the field of statistics is experiencing rapid growth fueled by emerging mathematical theories, technological innovations, fresh data streams stemming from contemporary challenges, and numerous connections forged between theory and application. The utilization of statistical theories and applications has become pervasive across various domains including actuarial science, biometrics, biomedical engineering, econometrics, environmental science, and financial markets. This Special Issue aims to highlight the significance and impact of statistics and its application, showcasing the latest advancements in statistical analysis, modeling, learning, and practical implementation. Topics include, but are not limited to, new developments in the following:
- Bayesian statistics;
- Model validation;
- Financial econometrics;
- Interval estimation and hypothesis testing;
- Quality control;
- Quantile regression—univariate and multivariate;
- Semi- and nonparametric modeling;
- Statistical learning with big data;
- Survival analysis;
- Genetics;
- Time series analysis—univariate and multivariate;
- Distributed/parallel computing.
Prof. Dr. Jiancheng Jiang
Dr. Shaoyu Li
Prof. Dr. Niansheng Tang
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- Bayesian statistics
- distributed computing
- variable selection
- nonparametric smoothing
- financial time series analysis
- analysis of genetics data
- high-dimensional data
- AI-assisted model validation
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Estimation of Heritability Using High-Dimensional Kernel Linear Mixed Models
Authors: Xiaoxi Shen; Qing Lu
Affiliation: Xiaoxi Shen, Department of Mathematics, Texas State University, San Marcos, TX, USA
Qing Lu, Department of Biostatistics, University of Florida, Gainesville, FL USA
Abstract: Recent studies have shown that genetic heritability is spread across multiple SNPs having contributions to the phenotypic traits. As a consequence, linear mixed models are commonly used in genetic studies and REstricted Maximum Likelihood (REML) estimators are used to produce unbiased estimators for the variance components. However, the relationship between SNPs and phenotypes are complex in general. To address the complex nature of genotype-phenotype relationships, we will first introduce the high-dimensional kernel linear mixed models. The REML equations will be derived followed by a discussion on the consistency of REML estimators and the heritability estimator for some commonly used kernel matrices. The validity of the theories is demonstrated via some simulation studies and real data analyses.
