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Robust Statistics in Action II
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Robust Statistics in Action II

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4123

Special Issue Editor

Prof. Dr. Marco Riani

E-Mail Website
Guest Editor
Department of Economics and Management and Interdepartmental Centre for Robust Statistics, University of Parma, Parma, Italy
Interests: all aspects of robust statistics (regression, multivariate analysis, classification and time series)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to announce a Special Issue on the practical use of robust statistics. I am soliciting manuscripts which show how the application of robust methods can help to address and solve real complex data problems in a way that is not possible using traditional methods. Suitable manuscripts could include but are not limited to the robust assessment of public health issues, fraud detection discovery, chemometrics and geochemistry, medical problems, all varieties of classification problems, predictive maintenance, and marketing applications in banks or firms. More generally, the purpose of the Special Issue is to show how robust statistics can be successfully applied to analyze multivariate complex data affected by different sources of heterogeneity, multiple outliers, and masking and swamping problems. Manuscripts applying robust statistics concepts to the modeling of the COVID-19 epidemic are especially welcome. Similarly, manuscripts introducing specific robust models which can be useful to practitioners are highly appreciated.

I look forward to receiving your submissions.

Prof. Dr. Marco Riani
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. Stats is an international peer-reviewed open access quarterly 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 1600 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

  • regression
  • multivariate analysis
  • time series
  • public health
  • classification (supervised and unsupervised) neural networks
  • factor analysis
  • fraud detection
  • predictive maintenance

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Related Special Issue

Published Papers (4 papers)

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Research

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12 pages, 1242 KB  
Article
Analysis of the Truncated XLindley Distribution Using Bayesian Robustness
by Meriem Keddali, Hamida Talhi, Ali Slimani and Mohammed Amine Meraou
Stats 2025, 8(4), 108; https://doi.org/10.3390/stats8040108 - 5 Nov 2025
Viewed by 66
Abstract
In this work, we present a robust examination of the Bayesian estimators utilizing the two-parameter Upper truncated XLindley model, a unique Lindley model variant, and the oscillation of posterior risks. We provide the model in a censored scheme along with its likelihood function. [...] Read more.
In this work, we present a robust examination of the Bayesian estimators utilizing the two-parameter Upper truncated XLindley model, a unique Lindley model variant, and the oscillation of posterior risks. We provide the model in a censored scheme along with its likelihood function. The topic of sensitivity and robustness analysis of the Bayesian estimators was only covered by a small number of authors. As a result, very few apps have been created in this field. The oscillation of the posterior hazards of the Bayesian estimator is used to illustrate the method. By using a Monte Carlo simulation study, we show that, with the correct generalized loss function, a robust Bayesian estimator of the parameters corresponding to the smallest oscillation of the posterior risks may be obtained; robust estimators can be obtained when the parameter space is low-dimensional. The robustness and precision of Bayesian parameter estimation can be enhanced in regimes where the parameters of interest are of small magnitude. Full article
(This article belongs to the Special Issue Robust Statistics in Action II)
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16 pages, 1288 KB  
Article
Quantile Estimation Based on the Log-Skew-t Linear Regression Model: Statistical Aspects, Simulations, and Applications
by Raúl Alejandro Morán-Vásquez, Anlly Daniela Giraldo-Melo and Mauricio A. Mazo-Lopera
Stats 2025, 8(3), 58; https://doi.org/10.3390/stats8030058 - 11 Jul 2025
Viewed by 712
Abstract
We propose a robust linear regression model assuming a log-skew-t distribution for the response variable, with the aim of exploring the association between the covariates and the quantiles of a continuous and positive response variable under skewness and heavy tails. This model [...] Read more.
We propose a robust linear regression model assuming a log-skew-t distribution for the response variable, with the aim of exploring the association between the covariates and the quantiles of a continuous and positive response variable under skewness and heavy tails. This model includes the log-skew-normal and log-t linear regression models as special cases. Our simulation studies indicate good performance of the quantile estimation approach and its outperformance relative to the classical quantile regression model. The practical applicability of our methodology is demonstrated through an analysis of two real datasets. Full article
(This article belongs to the Special Issue Robust Statistics in Action II)
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21 pages, 462 KB  
Article
Estimation of Standard Error, Linking Error, and Total Error for Robust and Nonrobust Linking Methods in the Two-Parameter Logistic Model
by Alexander Robitzsch
Stats 2024, 7(3), 592-612; https://doi.org/10.3390/stats7030036 - 21 Jun 2024
Cited by 10 | Viewed by 1797
Abstract
The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, [...] Read more.
The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, and Haebara linking are investigated in nonrobust and robust specifications in the presence of differential item functioning (DIF). M-estimation theory is applied to derive linking errors for the studied linking methods. However, estimated linking errors are prone to sampling error in estimated item parameters, thus resulting in artificially increased the linking error estimates in finite samples. For this reason, a bias-corrected linking error estimate is proposed. The usefulness of the modified linking error estimate is demonstrated in a simulation study. It is shown that a simultaneous assessment of the standard error and linking error in a total error must be conducted to obtain valid statistical inference. In the computation of the total error, using the bias-corrected linking error estimate instead of the usually employed linking error provides more accurate coverage rates. Full article
(This article belongs to the Special Issue Robust Statistics in Action II)

Other

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16 pages, 1699 KB  
Technical Note
Synthetic Hydrograph Estimation for Ungauged Basins: Exploring the Role of Statistical Distributions
by Dan Ianculescu and Cristian Gabriel Anghel
Stats 2025, 8(4), 100; https://doi.org/10.3390/stats8040100 - 17 Oct 2025
Viewed by 660
Abstract
The use of probability distribution functions in deriving synthetic hydrographs has become a robust method for modeling the response of watersheds to precipitation events. This approach leverages statistical distributions to capture the temporal structure of runoff processes, providing a flexible framework for estimating [...] Read more.
The use of probability distribution functions in deriving synthetic hydrographs has become a robust method for modeling the response of watersheds to precipitation events. This approach leverages statistical distributions to capture the temporal structure of runoff processes, providing a flexible framework for estimating peak discharge, time to peak, and hydrograph shape. The present study explores the application of various probability distributions in constructing synthetic hydrographs. The research evaluates parameter estimation techniques, analyzing their influence on hydrograph accuracy. The results highlight the strengths and limitations of each distribution in capturing key hydrological characteristics, offering insights into the suitability of certain probability distribution functions under varying watershed conditions. The study concludes that the approach based on the Cadariu rational function enhances the adaptability and precision of synthetic hydrograph models, thereby supporting flood forecasting and watershed management. Full article
(This article belongs to the Special Issue Robust Statistics in Action II)
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