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Mathematics
Special Issues
Statistical Methods for Reliability and Survival Analysis
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Statistical Methods for Reliability and Survival Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3652

Special Issue Editor

Prof. Dr. David D. Hanagal

E-Mail Website
Guest Editor
Department of Statistics, Savitribai Phule Pune University, Pune 411007, India
Interests: survival analysis; frailty models; reliability models; Bayesian inference; repair and replacement models; software reliability; multivariate models; nonparametric inference; fuzzy logics; quality loss index

Special Issue Information

Dear Colleagues,

Survival data is a term used for describing data that measures the time of a certain event. In survival analysis, the event may be death, the occurrence of disease (or complication), time to an epileptic seizure, the time it takes for a patient to respond to a therapy, or time from response until disease relapse (i.e., disease returns). Survival analysis consists of several areas, namely different censoring schemes, competing risks, semiparametric and parametric regression models, proportional hazard model, additive hazard models, nonparametric estimation, frailty models, accelerated life models, clinical trials, recurrent events, longitudinal data models, reliability models and so on.

We invite our colleagues to submit papers on areas related to the following keywords and in any closely linked field of study not mentioned specifically here.

Prof. Dr. David D. Hanagal
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

  • cox proportional hazard models
  • competing risks
  • censoring
  • truncation
  • additive hazard models
  • kaplan–Meier estimation
  • frailty models
  • accelerated life test
  • logrank test
  • tests for exponentiality
  • clinical trials
  • recurrent events
  • longitudinal data models
  • reliability models

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Published Papers (2 papers)

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19 pages, 6445 KiB  
Article
Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability
by Nikita V. Martyushev, Boris V. Malozyomov, Olga A. Filina, Svetlana N. Sorokova, Egor A. Efremenkov, Denis V. Valuev and Mengxu Qi
Mathematics 2023, 11(23), 4836; https://doi.org/10.3390/math11234836 - 30 Nov 2023
Cited by 14 | Viewed by 1171
Abstract
Improving the productivity and reliability of mining infrastructure is an important task contributing to the mining performance enhancement of any enterprise. Open-pit dump trucks that move rock masses from the mining site to unloading points are an important part of the infrastructure of [...] Read more.
Improving the productivity and reliability of mining infrastructure is an important task contributing to the mining performance enhancement of any enterprise. Open-pit dump trucks that move rock masses from the mining site to unloading points are an important part of the infrastructure of coal mines, and they are the main transport unit used in the technological cycle during open-pit mining. The failure of any of the mining truck systems causes unscheduled downtime and leads to significant economic losses, which are associated with the need to immediately restore the working state and lost profits due to decreased site productivity and a disruption of the production cycle. Therefore, minimizing the number and duration of unscheduled repairs is a necessity. The most time-consuming operations are the replacement of the diesel engine, traction generator, and traction motors, which requires additional disassembly of the dump truck equipment; therefore, special reliability requirements are imposed on these units. In this article, a mathematical model intended for processing the statistical data was developed to determine the reliability indicators of the brush collector assembly and the residual life of brushes of electric motors, which, unlike existing models, allow the determination of the refined life of the brushes based on the limiting height of their wear. A method to predict the residual life of an electric brush of a DC electric motor is presented, containing a list of controlled reliability indicators that are part of the mathematical model. Using the proposed mathematical model, the reliability of the brush-collector assembly, the minimum height of the brush during operation, and the average rate of its wear were studied and calculated. Full article
(This article belongs to the Special Issue Statistical Methods for Reliability and Survival Analysis)
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24 pages, 8073 KiB  
Article
Reliability Estimation of XLindley Constant-Stress Partially Accelerated Life Tests using Progressively Censored Samples
by Mazen Nassar, Refah Alotaibi and Ahmed Elshahhat
Mathematics 2023, 11(6), 1331; https://doi.org/10.3390/math11061331 - 9 Mar 2023
Cited by 5 | Viewed by 1646
Abstract
It often takes a lot of time to conduct life-testing studies on products or components. Units can be tested under more severe circumstances than usual, known as accelerated life tests, to reduce the testing period. This study’s goal is to look into certain [...] Read more.
It often takes a lot of time to conduct life-testing studies on products or components. Units can be tested under more severe circumstances than usual, known as accelerated life tests, to reduce the testing period. This study’s goal is to look into certain estimation issues related to point and interval estimations for XLindley distribution under constant stress partially accelerated life tests with progressive Type-II censored samples. The maximum likelihood approach is utilized to acquire the point and interval estimates of the model parameters as well as the reliability function under normal use conditions. The Bayesian estimation method using the Monte Carlo Markov Chain procedure using the squared error loss function is also provided. Moreover, the Bayes credible intervals as well as the highest posterior density credible intervals of the different parameters are considered. To make comparisons between the proposed methods, a simulation study is conducted with various sample sizes and different censoring schemes. The usefulness of the suggested methodologies is then demonstrated by the analysis of two data sets. A summary of the major findings of the study can be found in the conclusion. Full article
(This article belongs to the Special Issue Statistical Methods for Reliability and Survival Analysis)
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