Nonparametric Statistics and Biostatistical Methods

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 7478

Special Issue Editors

Institute of Biometry and Clinical Epidemiology, 10117 Berlin, Germany
Interests: biostatistical methods; nonparametric statistics; ranking methods; longitudinal data
Department of Statistics, University of Kentucky, Lexington, KY 40506, USA
Interests: multivariate statistics; high-dimensional; datarank-based methods
Department of Research and Innovation, Paracelsus Medical University, 5020 Salzburg, Austria
Interests: biostatistics and big medical data; intelligent data analytics
Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
Interests: statistical genetics; bioinformatics; biostatistics; nonparametrics; large sample theory; statistical computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Throughout the decades, biostatistics has established itself as an independent research area in biomedical sciences. In particular, nonparametric methods are fundamental in these, and related, areas. Applications are broad and range from general statistical inference to data modelling and prediction models. The aim of the present Symmetry Special Issue is highlighting recent and modern results on the use of nonparametric  methods and, especially, their applications.  We welcome methodological as well as applied papers on ranking procedures in general designs, survival analysis,, diagnostic tests, resampling methods (including bootstrap and permutation methods), clustered data, data modelling,  prediction models and regression analysis. Please note that all submitted papers must be within the general scope of the Symmetry journal.

Prof. Dr. Frank Konietschke
Prof. Dr. Solomon W. Harrar
Dr. Georg Johannes Zimmermann
Prof. Dr. Xin Gao 
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

  • Nonparametric methods
  • Ranking procedures
  • Bootstrap procedures
  • Survival analysis
  • Permutation methods
  • Statistical
  • Inference
  • Regression analysis

Published Papers (4 papers)

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

Research

Jump to: Review

24 pages, 379 KiB  
Article
Exact Boundary Correction Methods for Multivariate Kernel Density Estimation
by Ji-Yeon Yang
Symmetry 2023, 15(9), 1670; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15091670 - 30 Aug 2023
Viewed by 709
Abstract
This paper develops a method to obtain multivariate kernel functions for density estimation problems in which the density function is defined on compact support. If domain-specific knowledge requires certain conditions to be satisfied at the boundary of the support of an unknown density, [...] Read more.
This paper develops a method to obtain multivariate kernel functions for density estimation problems in which the density function is defined on compact support. If domain-specific knowledge requires certain conditions to be satisfied at the boundary of the support of an unknown density, the proposed method incorporates the information contained in the boundary conditions into the kernel density estimators. The proposed method provides an exact kernel function that satisfies the boundary conditions, even for small samples. Existing methods primarily deal with a one-sided boundary in a one-dimensional problem. We consider density in a two-sided interval and extend it to a multi-dimensional problem. Full article
(This article belongs to the Special Issue Nonparametric Statistics and Biostatistical Methods)
Show Figures

Figure 1

14 pages, 912 KiB  
Article
Quantifying the Effect Size of Exposure-Outcome Association Using δ-Score: Application to Environmental Chemical Mixture Studies
by Vishal Midya, Jiangang Liao, Chris Gennings, Elena Colicino, Susan L. Teitelbaum, Robert O. Wright and Damaskini Valvi
Symmetry 2022, 14(10), 1962; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14101962 - 20 Sep 2022
Cited by 1 | Viewed by 1259
Abstract
Epidemiologists often study the associations between a set of exposures and multiple biologically relevant outcomes. However, the frequently used scale-and-context-dependent regression coefficients may not offer meaningful comparisons and could further complicate the interpretation if these outcomes do not have similar units. Additionally, when [...] Read more.
Epidemiologists often study the associations between a set of exposures and multiple biologically relevant outcomes. However, the frequently used scale-and-context-dependent regression coefficients may not offer meaningful comparisons and could further complicate the interpretation if these outcomes do not have similar units. Additionally, when scaling up a hypothesis-driven study based on preliminary data, knowing how large to make the sample size is a major uncertainty for epidemiologists. Conventional p-value-based sample size calculations emphasize precision and might lead to a large sample size for small- to moderate-effect sizes. This asymmetry between precision and utility is costly and might lead to the detection of irrelevant effects. Here, we introduce the “δ-score” concept, by modifying Cohen’s f2. δ-score is scale independent and circumvents the challenges of regression coefficients. Further, under a new hypothesis testing framework, it quantifies the maximum Cohen’s f2 with certain optimal properties. We also introduced “Sufficient sample size”, which is the minimum sample size required to attain a δ-score. Finally, we used data on adults from a 2017–2018 U.S. National Health and Nutrition Examination Survey to demonstrate how the δ-score and sufficient sample size reduced the asymmetry between precision and utility by finding associations between mixtures of per-and polyfluoroalkyl substances and metals with serum high-density and low-density lipoprotein cholesterol. Full article
(This article belongs to the Special Issue Nonparametric Statistics and Biostatistical Methods)
Show Figures

Figure 1

27 pages, 1084 KiB  
Article
A Nonparametric Lack-of-Fit Test of Constant Regression in the Presence of Heteroscedastic Variances
by Mohammed M. Gharaibeh, Mohammad Sahtout, Haiyan Wang and Suojin Wang
Symmetry 2021, 13(7), 1264; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13071264 - 14 Jul 2021
Cited by 1 | Viewed by 1255
Abstract
We consider a k-nearest neighbor-based nonparametric lack-of-fit test of constant regression in presence of heteroscedastic variances. The asymptotic distribution of the test statistic is derived under the null and local alternatives for a fixed number of nearest neighbors. Advantages of our test [...] Read more.
We consider a k-nearest neighbor-based nonparametric lack-of-fit test of constant regression in presence of heteroscedastic variances. The asymptotic distribution of the test statistic is derived under the null and local alternatives for a fixed number of nearest neighbors. Advantages of our test compared to classical methods include: (1) The response variable can be discrete or continuous regardless of whether the conditional distribution is symmetric or not and can have variations depending on the predictor. This allows our test to have broad applicability to data from many practical fields; (2) this approach does not need nonlinear regression function estimation that often affects the power for moderate sample sizes; (3) our test statistic achieves the parametric standardizing rate, which gives more power than smoothing-based nonparametric methods for moderate sample sizes. Our numerical simulation shows that the proposed test is powerful and has noticeably better performance than some well known tests when the data were generated from high frequency alternatives or binary data. The test is illustrated with an application to gene expression data and an assessment of Richards growth curve fit to COVID-19 data. Full article
(This article belongs to the Special Issue Nonparametric Statistics and Biostatistical Methods)
Show Figures

Figure 1

Review

Jump to: Research

13 pages, 2197 KiB  
Review
An Empirical Comparative Assessment of Inter-Rater Agreement of Binary Outcomes and Multiple Raters
by Menelaos Konstantinidis, Lisa. W. Le and Xin Gao
Symmetry 2022, 14(2), 262; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14020262 - 29 Jan 2022
Cited by 12 | Viewed by 2884
Abstract
Background: Many methods under the umbrella of inter-rater agreement (IRA) have been proposed to evaluate how well two or more medical experts agree on a set of outcomes. The objective of this work was to assess key IRA statistics in the context of [...] Read more.
Background: Many methods under the umbrella of inter-rater agreement (IRA) have been proposed to evaluate how well two or more medical experts agree on a set of outcomes. The objective of this work was to assess key IRA statistics in the context of multiple raters with binary outcomes. Methods: We simulated the responses of several raters (2–5) with 20, 50, 300, and 500 observations. For each combination of raters and observations, we estimated the expected value and variance of four commonly used inter-rater agreement statistics (Fleiss’ Kappa, Light’s Kappa, Conger’s Kappa, and Gwet’s AC1). Results: In the case of equal outcome prevalence (symmetric), the estimated expected values of all four statistics were equal. In the asymmetric case, only the estimated expected values of the three Kappa statistics were equal. In the symmetric case, Fleiss’ Kappa yielded a higher estimated variance than the other three statistics. In the asymmetric case, Gwet’s AC1 yielded a lower estimated variance than the three Kappa statistics for each scenario. Conclusion: Since the population-level prevalence of a set of outcomes may not be known a priori, Gwet’s AC1 statistic should be favored over the three Kappa statistics. For meaningful direct comparisons between IRA measures, transformations between statistics should be conducted. Full article
(This article belongs to the Special Issue Nonparametric Statistics and Biostatistical Methods)
Show Figures

Figure 1

Back to TopTop