Journal of Data Science and Modeling

Abstract Progressive first-failure censoring schemes are potentially useful in practical applications where budget constraints exist or rapid testing is required. Moreover, several common sampling schemes such as first-failure censoring, progressive Type-II censoring, Type-II censoring, and complete sampling can be viewed as special cases of the progressive first-failure censoring scheme.
In this article, we propose a goodness-of-fit test statistic to assess whether a progressively first-failure-censored sample originates from a distribution belonging to the proportional hazard rate model. This model encompasses several well-known lifetime distributions, including the exponential, Rayleigh, Lomax, Burr Type-XII, Weibull, Gompertz, and Pareto distributions, among others.
We derive the null distribution of the proposed test statistic. Through Monte Carlo simulations, we evaluate the power of the proposed test under a range of different alternative distributions, assuming the null distribution to be exponential, Rayleigh, or Pareto. Finally, we present several numerical real-world examples to demonstrate the practical applicability of the proposed goodness-of-fit test. We also summarize the main findings and provide concluding remarks.

Abstract Abstract
In this article, unlike recently developed methods that can only distinguish associations between pairs of variables, Bayesian Multilevel Latent Class (BMLC) model for the multiple imputation of nested categorical data is considered which is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After presenting the model, I run the model on a real data set, that is, mathematics test of TIMSS 2015 which has missing data in itself, and at the end the completed data for both levels (Level 1, students and Level 2, schools) are obtained. To assess the performance of the BMLC model, we compare it with list wise deletion (LD) with the help of R software. Conclusion: Results show that the BMLC model has reliability and is able to cover the Bayesian estimator with lower risk in this research.
Keywords: test of TIMSS, Bayesian analysis, missing data, categorical data, Bayesian Multilevel Latent Class model.

Abstract Medical data are typically recorded across geographic regions and over successive time points, giving rise to spatio-temporal structures that present important statistical modelling challenges. In this study, we propose a spatio-temporal regression model based on the lognormal distribution within the structured additive regression framework. The lognormal assumption accommodates the positive skewness and heteroscedasticity of the incidence rate data. Bayesian inference for the model parameters was conducted using the integrated nested Laplace approximation (INLA), which delivers substantial computational gains over Markov chain Monte Carlo methods while maintaining comparable accuracy. We systematically compared four types of spatio-temporal interactions (Types I–IV) to identify the most parsimonious and best-fitting dependence structure. To evaluate performance, we applied the proposed model and several competitors to breast cancer incidence data from all provinces of Iran over the period 2010–2019. The results demonstrated the clear superiority of the lognormal structured additive regression (LNSTAR) model, with the Type II (temporal-only) interaction providing the best fit. Beyond its methodological contribution, this analysis provides actionable risk stratification to inform targeted screening policies in Iran.

Abstract Automated program repair (APR) for C/C++ programs is hindered by uncertain fault-localisation signals, large candidate spaces, costly test-based validation, and limited interpretability in learned or prompt-based repair decisions. This paper introduces Fuzzy-AutoRepair, an uncertainty-aware fuzzy learning-to-rank framework that prioritises explicit repair-pattern families prior to candidate generation. Unlike open-ended code generation, the proposed method represents each suspicious code region using 40 Boolean fault-local and contextual features and constrains candidate generation to 13 auditable AST-level repair-pattern families. The central novelty is a fuzzy inference layer that converts overlapping syntactic, data-dependence, control-context, API-call, enumeration, loop-header, and structural-risk evidence into linguistic repair suitability scores. These scores are fused with pairwise learning-to-rank relevance so that the exploration order is determined jointly by data-driven prediction and expert-readable fuzzy rationale, while type, scope, syntactic, and structural preconditions remain hard validity constraints. On 2,244 held-out pattern-selection samples and a 69-defect C/C++ benchmark, defect-level execution logs show that the balanced fuzzy-LTR configuration gives the strongest observed ranking and search-cost behaviour among the evaluated ordering variants. Compared with the LTR-only AutoRepair baseline, it improves Recall@1 from 0.45 to 0.50 (+11.1%), Recall@3 from 0.74 to 0.80 (+8.1%), and NDCG@5 from 0.78 to 0.84 (+7.7%). It also reduces generated candidates from 914 to 620 per defect (-32.2%), compilation attempts from 337 to 238 (-29.4%), and mean runtime from 49.38 to 41.70 minutes (-15.6%). The logs additionally show secondary observed differences in plausible repairs (33/69 versus 36/69) and correct-first repairs (14/69 versus 17/69).

Abstract This paper proposes a novel nonparametric support vector machine (SVM)-based approach for modeling and predicting fuzzy time-dependent data. Unlike traditional parametric methods such as ARIMA models and linear regression, which rely on rigid assumptions, the proposed framework leverages the flexibility of SVMs to capture complex, nonlinear temporal patterns in observations represented as fuzzy numbers. The nonparametric approach is chosen for its flexibility, robustness to model misspecification, and ability to accommodate irregular patterns and outliers common in fuzzy time series data. A practical example from software development quality monitoring motivates the need for this approach, where conventional SVM methods cannot directly handle fuzzy observations or uncertainty captured by spreads. To evaluate performance, the model is applied to both simulated fuzzy time series and a real-world dataset. A comprehensive set of evaluation criteria is employed: a similarity-based measure (MSM) for overall fuzzy similarity, root mean squared error (RMSE) for center accuracy, and average spread error (ASE) for spread accuracy. Empirical results demonstrate that the proposed method consistently outperforms existing methods across all three metrics. Furthermore, a comparison with a linear SVM model confirms the necessity of the nonlinear kernel for capturing complex temporal patterns, while the BDS test confirms the presence of nonlinear structure in the real dataset. The findings also indicate that the fitted models are robust and remain stable even in the presence of outliers. Overall, the results suggest that this SVM-based nonparametric framework offers a powerful, accurate, and robust tool for modeling fuzzy time-dependent data in various practical applications.

Abstract Persian Medicine (PM) pulsology is a central diagnostic modality for assessing
an individual’s temperamental state and cardiac strength; however, its practical
application is inherently subjective and dependent on practitioner expertise. To
enhance objectivity and reproducibility in PM diagnostics, this study proposes
an artificial neural network (ANN) framework for the quantitative estimation of
four core temperamental qualities—warmness, coldness, wetness, and dryness—
together with cardiac strength, based solely on measurable pulse characteristics
Clinical data were collected from 69 individuals, comprising 11 pulse-derived features
and gender as inputs, with six corresponding diagnostic outputs. A multilayer
perceptron (MLP) architecture was trained and optimized using two learning
algorithms: Levenberg–Marquardt (LM) and Scaled Conjugate Gradient (SCG).
Comparative evaluation demonstrated the superiority of the SCG-trained network,
with an optimal configuration of 12 hidden neurons. This model achieved a test
accuracy of 96.43% and a low mean squared error (MSE) of 0.0139. Notably, cardiac
strength was predicted with 100% accuracy, while temperamental qualities
were classified with accuracies ranging from 71.43% to 92.86%. To enhance interpretability,
a sensitivity analysis was conducted, revealing pulse strength and pulse
frequency as the most influential predictors across multiple diagnostic outputs.
The proposed ANN-based system provides a stable and objective computational
surrogate for traditional PM pulsology. It offers practical utility for practitioner
training, supports diagnostic standardization, and establishes a methodological
foundation for future integrative and intelligent diagnostic platforms in Persian
Medicine.

Abstract This paper introduces a novel parametric activation function based on Wendland radial basis functions (RBFs) for deep neural networks. Wendland RBFs, known for their compact support, smoothness, and positive definiteness in approximation theory, are adapted to address limitations of traditional activation functions like ReLU, sigmoid, and tanh. The proposed enhanced Wendland activation combines a standard Wendland component with linear and exponential terms, offering tunable locality, improved gradient propagation, and enhanced stability during training. Theoretical analysis rigorously examines derivative behavior, smoothness, gradient flow, and saturation properties, demonstrating advantages over ReLU, GELU, and Swish including strictly positive gradients, $C^2$-continuity, near-unity gradient decay, and well-conditioned Jacobians. Empirical experiments on synthetic tasks and benchmark datasets confirm competitive performance, with comprehensive diagnostic analyses validating the theoretical claims. Results show that the Wendland-based activation achieves superior accuracy in certain scenarios, particularly in regression tasks, while maintaining computational efficiency. The study bridges classical RBF theory with modern deep learning, suggesting that Wendland activations can mitigate overfitting and improve generalization through localized, smooth transformations.