The reflected-shifted-truncated Maxwell distribution: properties and applications to reliability analysis
Main Article Content
Abstract
Modeling lifetime and reliability data requires flexible probability distributions capable of capturing diverse hazard rate behaviors and skewness patterns. Classical models often fail to adequately represent left-skewed data with bounded ranges. To address this limitation, the Reflected-Shifted-Truncated Maxwell (RSTM) distribution is introduced as an extension of the classical Maxwell model through reflection, shifting, and truncation. Key statistical properties including moments, hazard rate behavior, and stress–strength reliability are derived. Parameters are estimated using the maximum likelihood method for both complete and right-censored data, and estimator performance is assessed via simulation studies. The effectiveness of the RSTM distribution is illustrated through two fiberglass strength datasets, representative of left-skewed lifetime data. Comparative analysis based on information-theoretic measures demonstrates that the RSTM distribution consistently outperforms competing models, underscoring its potential as a robust tool for modeling leftskewed lifetime and reliability data.
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
1. Mohd, I. and Sharma, A. K. Bayesian Estimation and Prediction for Inverse Power Maxwell Distribution with Applications to Tax Revenue and Health Care Data. Journal of Modern Applied Statistical Methods 23 (2024).
2. Bekker, A. and Roux, J. J. J. Reliability characteristics of the Maxwell distribution: A Bayes estimation study. Communications in Statistics-Theory and Methods 34, 2169–2178 (2005).
3. Boltzman, L. Über die Ableitung der Grundgleichungen der Capillarität aus dem Principe der virtuellen Geschwindigkeiten, Pogg. Annalen der Physik und Chemie 141, 582–590 (1870).
4. Chaturvedi, A. and Rani, U. Classical and Bayesian reliability estimation of the generalized Maxwell failure distribution. Journal of Statistical Research 32, 113–120 (1998).
5. Dey, S., Altun, E., Kumar, D. and Ghosh, I. The Reflected-Shifted-Truncated Lomax Distribution: Associated Inference with Applications. Annals of Data Science 10, 805––828 (2023).
6. Dey, S., Waymyers, S. D. and Kumar, D. The reflected-shifted-truncated lindley distribution with applications. Stochastics and Quality Control 35, 67–77 (2020).
7. Gilchrist, W. Statistical modelling with quantile functions (CRC Press, 2000).
8. Glaser, R. E. Bathtub and related failure rate characterizations. Journal of the American Statistical Association 75, 667–672 (1980).
9. Kumari, A., Kumar, K. and Kumar, I. Bayesian and classical inference in Maxwell distribution under adaptive progressively Type-II censored data. International Journal of System Assurance Engineering and Management, 1–22 (2023).
10. Maxwell, J. C. and Clerk, J. Illustrations of the dynamical theory of gases. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 20, 21–37 (1860).
11. Mohieddine, A. and Dalia, Z. The Weibull-generalized shifted geometric distribution: properties, estimation, and applications. AIMS Mathematics 10, 9773–9804 (2025).
12. Shaked, M. and Kumar, S. and George, J. Stochastic orders (Springer, 2007).
13. Shams, M. and Mirzaie, M. A. Statistical Inference and Simulation for the Maxwell-Boltzmann Distribution. Advanced Theory and Simulations 8, 2500148 (2025).
14. Sharma, V.K., Singh, S. K. and Singh, U. A new upside-down bathtub shaped hazard rate model for survival data analysis. Applied Mathematics and Computation 239, 242–253 (2014).
15. Smith, R. L. and Naylor, J. C. A comparison of maximum likelihood and Bayesian estimators for the threeparameter Weibull distribution. Journal of the Royal Statistical Society Series C: Applied Statistics 36, 358–369 (1987).
16. Tomer, S. K. and Panwar, M. S. A review on Inverse Maxwell distribution with its statistical properties and applications. Journal of Statistical Theory and Practice 14, 1–25 (2020).
17. Tomer, S. K. and Panwar, M. S. Estimation procedures for Maxwell distribution under type-I progressive hybrid censoring scheme. Journal of Statistical Computation and Simulation 85, 339–356 (2015).
18. Tyagi, R. K. and Bhattacharya, S. K. Bayes estimation of the Maxwell’s velocity distribution function. Statistica 29, 563–567 (1989).
19. Weisstein, E. W. Leibniz integral rule. https://mathworld. wolfram. com/ (2003).