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Mathematics
Special Issues
Statistical Analysis and Data Science for Complex Data
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Statistical Analysis and Data Science for Complex Data

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 952

Special Issue Editor

Dr. Li-pang Chen

E-Mail Website
Guest Editor
Department of Statistics, National Chengchi University, Taipei 116, Taiwan
Interests: graphical models; high-dimensional data analysis; machine learning; measurement error and error classification; survival analysis

Special Issue Information

Dear Colleagues,

Nowadays, thanks to the rapid development of technology, datasets can be collected easily in many fields, such as biology, manufacturing, and so on. Typically, given a dataset, one may encounter situations wherein (i) the sample size is large or (ii) the dimension of variables is large, yielding so-called big data or high-dimensional data, respectively. However, rare samples or variables are informative in data analysis. On the other hand, datasets usually contain complex structures caused by the collection procedure, such as censoring, measurement errors, or missingness. With noisy data, it becomes more challenging to choose informative subdata, detect important variables, or conduct analyses. In light of these challenges, this Special Issue aims to provide a platform to publish novel statistical methods and algorithms that handle those complex structures in various research fields. Topics of interest for this Special Issue include but are not limited to biostatistics, bioinformatics, causal inference, meta analysis, statistical process control, and survival analysis.

Dr. Li-pang Chen
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. 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

  • algorithm
  • big data
  • high dimensionality
  • noisy data

Published Papers (2 papers)

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Research

22 pages, 1823 KiB  
Article
Computation of the Mann–Whitney Effect under Parametric Survival Copula Models
by Kosuke Nakazono, Yu-Cheng Lin, Gen-Yih Liao, Ryuji Uozumi and Takeshi Emura
Mathematics 2024, 12(10), 1453; https://doi.org/10.3390/math12101453 - 8 May 2024
Viewed by 397
Abstract
The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence [...] Read more.
The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands. Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets. Full article
(This article belongs to the Special Issue Statistical Analysis and Data Science for Complex Data)
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16 pages, 393 KiB  
Article
Data-Adaptive Multivariate Test for Genomic Studies Using Fused Lasso
by Masao Ueki
Mathematics 2024, 12(10), 1422; https://doi.org/10.3390/math12101422 - 7 May 2024
Viewed by 291
Abstract
In genomic studies, univariate analysis is commonly used to discover susceptible variants. It applies univariate regression for each variant and tests the significance of the regression coefficient or slope parameter. This strategy, however, may miss signals that are jointly detectable with other variants. [...] Read more.
In genomic studies, univariate analysis is commonly used to discover susceptible variants. It applies univariate regression for each variant and tests the significance of the regression coefficient or slope parameter. This strategy, however, may miss signals that are jointly detectable with other variants. Multivariate analysis is another popular approach, which tests grouped variants with a predefined group, e.g., based on a gene, pathway, or physical location. However, the power will be diminished if the modeling assumption is not suited to the data. Therefore, data-adaptive testing that relies on fewer modeling assumptions is preferable. Possible approaches include a data-adaptive test proposed by Ueki (2021), which applies to various data-adaptive regression models using a generalization of Yanai’s generalized coefficient of determination. While several regression models are possible choices for the data-adaptive test, this paper focuses on the fused lasso that can count for the effect of adjacent variants and investigates its performance through comparison with other existing tests. Simulation studies demonstrate that the test using fused lasso has a high power compared to the existing tests including the univariate regression test, saturated regression test, SKAT (sequence kernel association test), burden test, SKAT-O (optimized sequence kernel association test), and the tests using lasso, ridge, and elastic net when assuming a similar effect of adjacent variants. Full article
(This article belongs to the Special Issue Statistical Analysis and Data Science for Complex Data)
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