ArticleJournal of applied statistics2026
One parameter stochastic distribution of ratio for cross-disciplinary applications: system reliability, healthcare, and energy consumption.
Article in Journal of applied statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical modeling of real-life phenomena analyzes their behavior and patterns by quantifying the inherent uncertainty and describes the possible outcomes which help to make proactive strategies attaining optimum results. This paper introduces a one parameter stochastic distribution of ratio derived from the bivariate conditional Weibull distribution to model the data sets from diverse fields such as system reliability, healthcare and energy consumption. Some important features and statistical properties of the proposed distribution are presented. A simulation study is conducted by using the acceptance-rejection method to evaluate the stability of the model parameter based on its average estimated values, standard errors, and biases. The parameter of the proposed distribution is estimated by using maximum likelihood and Bayesian methods. The proposed model and some extant models are employed on the ratio of downtimes to the total times, case fatality rate and proportion of cooling energy. The comparison of the results establishes the competency of the proposed model. The estimated quantiles for the three different data sets provide insights about the potential range of system reliability in terms of downtime to the total time, case fatality rate and energy consumption for cooling the buildings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.