Journal of Reliability and Statistical Studies

Fuzzy Reliability Estimation Within a Stress-Strength Framework Incorporating Distortion Functions

K. Sruthi — 2026-01-13

The primary focus of this paper is to present an estimation of fuzzy system reliability for a stress-strength model that accounts for uncertainty in the parameters of the distribution function. A drawback of existing methods in the literature is that they do not consider data uncertainty or fuzziness when estimating system reliability. To obtain a more realistic estimation, it is necessary to incorporate the uncertainty present in real-world scenarios. In this work, we incorporate both a distortion function and data fuzziness to estimate system reliability using the stress-strength model, resulting in a more practical approach. We estimate reliability using a suitable distortion function with fuzzy parameters. Specifically, Power, Dual Power, and Piece-wise Type II distortion functions are considered in conjunction with a standard exponential lifetime distribution. Additionally, we obtain a system reliability estimate under a dynamic stress-strength model using a power distortion function with a fuzzy parameter. Several numerical examples are computed to illustrate our approach to fuzzy system reliability estimation. To demonstrate practical application, an illustrative example using simulated estimates is presented for a real-life problem, the stress-strength reliability of reinforced concrete roofs. Finally, a discussion compares the proposed method to an existing method using numerical values.

Performance Evaluation of a Parallel System with Asymmetric Units and Dynamic Repair Prioritization

Amit Kumar — 2026-01-13

In this paper, a parallel system consisting of two non-identical units has been studied. These dissimilar units of system are assumed to have different characteristics and different types of failure modes. All types of failures are treated by a single repairman who is made available to the system within no time. Some constraints for repair priorities are introduced for the system, which will vary as per unit undergoing failure. Failure rates of both units are assumed to follow exponential distribution, while repair rates are supposed to have any arbitrary distribution. To evaluate the system’s reliability measures, Regenerative Point Technique has been used. Also, graphical and numerical representations are provided to illustrate the variations in these measures with respect to all parameters involved within system.

Bivariate Normal Distribution for Indeterminacy: Characteristics and Data Generation Algorithm

Muhammad Aslam — 2026-01-13

The existing bivariate normal distribution and its related algorithms in classical statistics cannot account for the degree of indeterminacy when applied under uncertainty. To address this gap, the main objective of this manuscript is to introduce bivariate neutrosophic random variables and study their properties through expectation and variance. In this paper, we also propose the neutrosophic bivariate normal distribution along with some of its key properties. Furthermore, we develop an algorithm based on the proposed distribution to generate imprecise data. A detailed simulation is carried out to examine the effect of the degree of indeterminacy on the data. The comparative study reveals that the variates produced by the proposed algorithm differ from those generated by the existing algorithm. To demonstrate its practical use, we provide a numerical example applying the bivariate normal distribution. Based on the simulation, comparative study, and numerical example, we recommend incorporating the degree of indeterminacy when generating data from the bivariate normal distribution under uncertainty.

A Factor Analysis Approach to Evaluate Batting and Bowling Performance in International Cricket Formats (Tests, ODIs, and T20Is)

Kuldeep Dahal — 2026-01-13

Batting and bowling performances are crucial to evaluating the overall contribution of cricket players across all international formats. This study applies factor analysis to assess player performance in Test, ODI, and T20I formats. The dataset comprises 192 players from the 2021–2023 ICC World Test Championship (Test), 149 players from the 2023 ICC Cricket World Cup (ODI), and 193 players from the 2022 ICC T20 World Cup (T20I). The analysis reveals that in the limited-overs formats – ODIs and T20Is – batting performance tends to dominate, accounting for 45.66% and 46.77% of the variance, respectively, compared to bowling performance, which contributes 34.36% in ODIs and 35.61% in T20Is. However, the test format exhibited a near-equal distribution of variance with batting 40.61% and bowling 39.80% of the total variance.

Advanced Row-Column Designs for Test Vs Single Control Comparisons in Animal Experiments

Anindita Datta — 2026-01-22

In animal studies where experimental units are influenced by two sources of variation, row-column designs are commonly employed. When there is a large number of treatments but limited experimental resources, Generalized Row-Column (GRC) designs become useful. These designs enable multiple experimental units at each row-column intersection, optimizing resource use. Historically, GRC designs have been focused on supporting all possible pairwise comparisons among treatments. However, in many biomedical or pharmaceutical experiments, the main goal is not to compare all treatments, but rather to evaluate new (test) treatments against a standard (control) treatment. In such situations, the emphasis is placed on estimating the treatment-control contrast as precisely as possible. To meet this need, we introduce a balanced version of GRC designs specifically for treatment-control comparisons, and we propose a class of partially balanced GRC designs. These modifications aim to improve the precision of contrast estimation between test and control treatments, while still ensuring structural balance within rows and columns.

A Piecewise Smooth Approach to Modeling Innovation Adoption Under Time-Varying External Influences

Khushboo Garg — 2026-02-18

Innovation diffusion modeling plays a crucial role in understanding how new technologies, products, or ideas spread through a population over time. Classical approaches such as the Bass model assume smooth and continuous adoption patterns, which often fail to capture abrupt changes caused by market dynamics, technological disruptions, or policy interventions. This study develops a piecewise smooth diffusion framework that extends the Bass innovation diffusion model to incorporate random shifts across different time intervals. The framework introduces modulation functions that allow both gradual transitions and abrupt perturbations in adoption rates, thereby reflecting the non-linear dynamics of real-world diffusion. Stability analysis is conducted to examine the robustness of the system. The model is applied to historical datasets on cassette sales, compact discs, and physical video records. Empirical evaluation demonstrates that the piecewise approach provides superior fitting accuracy compared with standard Bass formulations, while also reducing parameter estimation errors. The findings highlight the value of modeling random shifts in diffusion processes, offering new insights for understanding technology substitution and for designing adaptive marketing and policy strategies.

Cesarean Delivery-Emergency or Elective in India: Evidence from NFHS-V

Brijesh P. Singh — 2026-02-18

The number of caesarean deliveries worldwide has been rising, which raises concerns about whether choosing this operation is suitable. Using both bivariate and multivariate logistic regression techniques, the current study aims to investigate the factors that influence the preference for caesarean delivery in India in order to ascertain whether the procedure is elective or emergency. It also looks at the relationship between the risk of caesarean delivery and women’s pre-pregnancy obesity, height, delivery complications, preferred place of antenatal care visit as well as place of delivery, desired child, and sociodemographic variables. Results show that the risk of undergoing Cesarean section in the private sector is about four times higher than that in the public sector. The younger and educated women are more likely to prefer Cesarean delivery as compared to their counterparts. It’s likely that this medical treatment is being abused for financial gain in the private sector or that women are choosing to forego labour discomfort on purpose.

Curated Hinglish Dataset for Deep Learning-Based Misogyny Detection

Deepti Negi — 2026-03-15

Social networking sites serves as influential medium for sharing information and communication; however, their mostly unregulated and open frameworks have also turned them into fertile ground for the dissemination of offensive content. The simplicity of sharing content, coupled with user anonymity and vast reach, facilitates the swift circulation of offensive, abusive, and discriminatory remarks. Engagement-driven algorithms may unintentionally promote such harmful content, increasing its visibility and impact. Consequently, offensive content on platforms like Twitter, YouTube, Facebook, and Reddit frequently gains traction, fuelling online hostility, social division, and tangible real-world effects. Offensive content about women is a prevailing subject on social media platforms. Instances of misogyny are disproportionately represented on social media platforms and misogyny is a substantial societal concern which needs to be addressed.

While exhaustive research work has been done for offensive language detection in monolingual settings, the domain of misogyny detection in code-mixed texts is relatively underexplored and there is lack of studies that tackle misogyny detection in under-resourced languages. One of the major causes is unavailability of appropriate Hindi-English mixed-coded language dataset. Therefore, in attempt to bridge this research gap our study focuses on developing a dataset and leveraging deep learning techniques on this high-quality curated dataset containing Hindi-English code-mixed comments from multiple social media platforms. This dataset contains 17,234 comments from different social media platforms, annotated manually into misogynistic and non-misogynistic based on the content. Our study also demonstrates a detailed comparison between baseline machine learning, deep learning, and transformer-based approaches utilising our own curated Hinglish dataset. The results indicated that fine-tuned BERT outperformed the deep learning algorithms with highest 0.92 accuracy.

An Educational Tutorial on Fisher’s Exact Test for Medical Researchers

Farzan Madadizadeh — 2026-04-05

Fisher’s exact test is a fundamental statistical tool for analyzing associations in 2×2 contingency tables, especially when dealing with small samples or sparse data common in preliminary medical studies. Despite its widespread use, misconceptions regarding its assumptions, application criteria, and interpretation persist. This tutorial provides a structured, practical guide to Fisher’s exact test. We begin with the theoretical foundation, contrasting it with the chi-square test and explaining its reliance on the hypergeometric distribution. The core of the tutorial features step-by-step manual calculations for educational clarity, followed by practical implementation guides using R, SPSS, and Stata. We address common pitfalls, including misuse in large samples, confusion between one- and two-tailed p-values, and the need for multiple testing corrections. Extensions to larger tables via the Fisher-Freeman-Halton test and Monte Carlo simulation are also discussed. By integrating theory with actionable examples, this tutorial aims to enhance statistical literacy and ensure the accurate application of Fisher’s exact test in clinical and epidemiological research, thereby improving the reliability and reproducibility of findings.

Prediction of Wheat Yield Through Soil Nutrient: Machine Learning and Feature Selection Approaches

Afshan Tabassum — 2026-04-05

Wheat productivity is greatly influenced by soil nutrient variability, especially in areas with agroclimatic diversity like Jammu. The study analyzed a comprehensive dataset comprising 5,196 soil samples and corresponding wheat yield records from the districts of Jammu, Rajouri, and Kathua in Jammu region. This work predicted wheat yield from soil factors using machine learning (ML) models. The ML models used include Random Forest, Gradient Boosting, Support Vector Regression, and Decision Trees, in conjunction with embedded and wrapper-based feature selection methods. The soil variables analyzed in this study included pH, EC, OC, N, P, K, S, Cu, Zn, Mn, and Fe. Among the tested machine learning models, Random Forest yielded the highest predictive accuracy, with RMSE = 2.6570, MAE = 2.1578, and MAPE = 44.87%. Recursive Feature Elimination identified an optimal subset of 10 soil predictors, with S, Mn, Zn, and EC emerging as the most influential variables for wheat yield estimation. In all models, sulfur (S), manganese (Mn), electrical conductivity (EC), and zinc (Zn) were consistently found to be the most significant predictors. In comparison to other models, Random Forest and Support Vector Machines generated more reliable and broadly applicable predictions, according to stability study using k-fold cross-validation. The study highlights the effectiveness of machine learning techniques, particularly Random Forest, in predicting wheat yield from soil parameters. The consistent importance of micronutrients like S, Mn, and Zn underscores the need for micronutrient-focused soil management strategies. These findings demonstrate the usefulness of data-driven approaches in heterogeneous soil and climatic conditions.