https://jirss.irstat.ir/ Journal of the Iranian Statistical Society en daily 1 Mon, 01 Dec 2025 00:00:00 +0330 Mon, 01 Dec 2025 00:00:00 +0330 https://jirss.irstat.ir/article_733687.html In design of experiments, optimal design is an important approach that maximizes the chances of experimental success. A- and D-optimality are well-known criteria for identifying optimal designs. In nonlinear models, these criteria depend on unknown parameters, complicating the design derivation. This paper uses the Bayesian method to address this, deriving A- and D-optimal designs for EMAX, log-linear, and LINEXP models with three or four parameters, using uniform priors. Optimal designs with minimum support points are obtained, with varying weights. These designs serve as benchmarks for evaluating practical alternative designs. Two alternatives were assessed, showing over 80\% efficiency in most models compared to A- and D-optimal designs. The computations in this study were performed using a numerical nonlinear approach, specifically the NLPSolve method, which is included in the Optimization package in Maple software. https://jirss.irstat.ir/article_733688.html The accurate diagnosis of infectious diseases such as COVID-19 requires statistically reliable classification methods capable of handling complex, heterogeneous, and imbalanced data. In this study, several statistical and machine learning algorithms --logistic regression, linear discriminant analysis, k-nearest neighbors, decision tree, and random forest --were comparatively evaluated using clinical and laboratory data from 506 hospitalized patients in Rafsanjan, Iran. The dataset included 27 categorical and 11 quantitative variables. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. Model performance was assessed using a comprehensive set of criteria, including accuracy, sensitivity, specificity, positive and negative predictive values (NPV), and the area under the ROC curve. The comparative analysis showed that RF and LR achieved the best overall performance, while SMOTE improved sensitivity and NPV at the expense of specificity. The findings emphasize the importance of appropriate imbalance correction and multi-metric evaluation in developing statistically robust diagnostic models for medical data. https://jirss.irstat.ir/article_733689.html In this paper, we propose a generalized cross entropy between the distributions of the ith order statistic and the parent random variable X, defined using the quantile function. This method is more flexible than traditional PDF-based measures, particularly in situations where estimating the underlying density is difficult or unreliable. We investigate the properties of this measure and present examples to illustrate these concepts. Furthermore, we introduce a residual version of the quantile based generalized cross entropy between the distributions of the ith order statistic and the parent random variable X, along with some characterization results. Comparative analyses using simulation and real data indicate that the proposed measure provides improved interpretability and robustness relative to the quantile based Kerridge inaccuracy measure. This study effectively connects theoretical development with practical application, contributing to the field of statistical analysis. https://jirss.irstat.ir/article_733691.html ‎‎In this paper, we investigate the stochastic properties of spacings among order statistics derived from a sample of independent, non-negative random variables that are divided into two groups with different distributions. Previous studies have shown that when these distribution functions are exponential distributions with specified hazard rates, the likelihood ratio ordering holds among the spacings under specific conditions. The present work extends these results by considering more general continuous distribution functions. We identify the necessary conditions on the parent distribution functions for preserving the likelihood ratio ordering among spacings in general settings. The comparison results enhance our understanding of stochastic ordering theory and provide valuable insights for applications in reliability‎, ‎survival analysis‎, ‎and related fields‎, ‎aiding in the development of more flexible and accurate statistical models‎. https://jirss.irstat.ir/article_733693.html The generalized linear models (GLMs) use Gamma-Pareto regression Model (G-PRM) to address the sensitivity of influential observations. Difference of Fits (DFFITS) is a popular technique for identifying influential observations. We apply DFFITS to the G-PRM with various residuals. We present illustrative real and simulated data. One class of adjusted Pearson residuals is more effective in detecting influential observations, considering small or large dispersion parameters. We calculate detection percentages to evaluate the proposed procedure's performance, replicating the process 10,000 times.The generalized linear models (GLMs) use Gamma-Pareto regression Model (G-PRM) to address the sensitivity of influential observations. Difference of Fits (DFFITS) is a popular technique for identifying influential observations. We apply DFFITS to the G-PRM with various residuals. We present illustrative real and simulated data. One class of adjusted Pearson residuals is more effective in detecting influential observations, considering small or large dispersion parameters. We calculate detection percentages to evaluate the proposed procedure's performance, replicating the process 10,000 times. https://jirss.irstat.ir/article_733692.html The SIRD (Susceptible–Infected–Recovered–Deceased) model is a standard framework for analyzing infectious disease dynamics. Classical continuous-time Markov formulations assume constant transition rates and memoryless (exponential) sojourn-times, which may oversimplify empirical epidemic processes. In this study, the Markov SIRD model is employed as a baseline, with transition parameters estimated analytically via maximum likelihood. To assess the validity of the exponential sojourn-time assumption, a semi-Markov framework is introduced exclusively for duration analysis of the infected state. Specifically, the sojourn-times associated with recovery (I --> R) and death (I -->D) transitions are modeled using exponential and Weibull distributions and compared using likelihood-based criteria. Using COVID-19 data from the Special Region of Yogyakarta, Indonesia, the results show that Weibull distributions provide a substantially better fit than the exponential assumption for both recovery and mortality durations. These findings indicate significant deviations from the memoryless assumption underlying the Markov model. This study does not construct a full dynamic semi-Markov epidemic simulator; instead, the semi-Markov framework is used to statistically characterize and evaluate the temporal structure of infected-state durations. The results highlight the importance of realistic sojourn-time modeling for understanding epidemic progression and for assessing the limitations of classical Markov-based epidemic models. https://jirss.irstat.ir/article_735131.html In this paper, we investigate a new approach of fuzzy regression analysis based on support vectors when the available data and error variable are fuzzy quantities. In this approach, based on the concept of the distance between two parallel hyper planes, we obtain the marginal hyper planes and then, based on some constraints on the fuzzy data, we present an optimization problem to estimate the parameters of fuzzy regression model. The proposed method is investigated in two cases: with fuzzy fixed error and with fuzzy variable errors. To evaluate the proposed support vector fuzzy regression (SVFR) models, we present two indices of goodness of fit. Based on these indices, the presented SVFR models are compared with some other approaches on the numerical and simulated examples. https://jirss.irstat.ir/article_733690.html The prediction of credit risk is of great economic importance for banks and financial institutions, leading to the utilization of various methods in developing predictive models. This study introduces a credit risk prediction model that combines the support vector machine (SVM) with a genetic algorithm (GA) to aid credit decision-making by managers. While SVM is a reliable classification method, its performance can be influenced by factors such as model shape, parameter setting, and feature selection. To address these challenges, a novel approach is proposed that employs GA to optimize feature selection and parameter settings within the SVM framework.The proposed model is compared against alternative models including neural network, logistic regression, random forest, and decision tree. The study utilizes data from Bank of Yazd Province, with a sample size of 1876 customers divided into two groups: those who defaulted on their credit obligations and those who fulfilled them. The results demonstrate that the GA-SVM model serves as a suitable alternative for credit risk prediction, outperforming other models in terms of predictive power. Furthermore, the proposed model offers the benefit of feature selection, enabling financial institutions to identify potential risks and implement preventive measures. The use of GA in conjunction with SVM also facilitates the identification of optimal SVM parameter values, thereby enhancing the overall performance of the model. In conclusion, the proposed GA-SVM model emerges as a valuable tool for credit decision-making and risk management within banks and financial institutions. Further optimization can be achieved by exploring other meta-heuristic optimization algorithms. https://jirss.irstat.ir/article_735954.html This study proposes a novel estimator for the finite population variance under simple random sampling. The estimator utilizes dual auxiliary information by incorporating the empirical cumulative distribution function (ECDF) of an auxiliary variable. The ECDF, which represents the stochastic process over the unit interval [0,1], is employed to enhance the estimation precision. The performance of the proposed estimator is evaluated through a comprehensive analysis. First, the bias and mean squared error (MSE) of the estimator are derived analytically. Second, a simulation study is conducted to investigate the estimator's behavior under various parametric settings. Finally, an empirical comparison is made with several well-established estimators using five real-world datasets. The results consistently demonstrate the superiority of the proposed estimator in terms of both bias and MSE, suggesting its practical utility. https://jirss.irstat.ir/article_737173.html This paper proposes new nonparametric tests for symmetry based on cumulative past extropy and cumulative residual extropy of record values, motivated by a recent characterization of symmetric distributions by Gupta and Chaudhary (2024). The proposed estimators are inspired by the methodology introduced by Vasicek (1976). The proposed tests do not require estimation of the centre of symmetry, making them robust and easy to implement. Their asymptotic properties and consistency are established, and critical values are obtained via Monte Carlo simulations. Power is evaluated under various asymmetric alternatives. Results show that the proposed tests perform competitively and often outperform existing symmetry tests while maintaining the nominal significance level. Application of the test to six real-world datasets confirms its effectiveness in detecting symmetric and asymmetric behavior through significant p-values. https://jirss.irstat.ir/article_737192.html Reliability assessment of complex systems requires accurate modeling of failure mechanisms induced by random external shocks. Classical shock models often focus either on shock magnitudes or on the inter-arrival times between successive shocks, whereas many real-world systems are affected by the joint impact of these two factors. This paper introduces a mixed shock model for systems whose failure is governed by both shock magnitudes and inter-arrival times. In the proposed model, system failure occurs either when the inter-arrival time falls within the interval [α, δ], where 0 ≤ α < δ, or when the magnitude of a single shock exceeds a critical threshold γ. The probability distribution of the system’s stopping time is derived, and the reliability properties of the system’s lifetime are investigated. Numerical examples are provided to illustrate the theoretical findings. https://jirss.irstat.ir/article_737174.html In queueing theory, system is evaluated using performance merics such as the average number of of customers in the queue and system, and the average waiting time. One of the most important parameters for these metrics is the traffic intensity, which must be estimated. This paper proposes a new estimation method and compares it with existing approaches. We focus on the M/M/1/K single-server queueing model ( finite capacity) and estimate the traffic intensity using Bayesian, E-Bayesian, hierarchical Bayesian, and a new EE-Bayesian method. Beacause reducing costs and minimizing customer waiting time are central goales in queueing systems, We consider an estimator suitable if it minimizes average customer waiting time. Using Monte Carlo simulation and a real dataset, We demonstrate the superiority of the proposed method over other estimators. https://jirss.irstat.ir/article_737191.html Bayesian neural networks provide a probabilistic framework for nonlinear regression by combining the expressive flexibility of neural networks with principled uncertainty quantification. However, practical implementation remains challenging because posterior inference is computationally demanding and often requires approximate methods. This study presents a comparative analysis of several inference approaches for Bayesian neural-network regression, including Hamiltonian Monte Carlo, the No- U-Turn Sampler and variational inference, with emphasis on predictive uncertainty, posterior calibration and computational efficiency. The results show that different inference strategies often achieve comparable predictive accuracy, whereas substantially larger differences emerge in uncertainty quantification and scalability. Sampling-based approaches provide more reliable posterior characterization and better-calibrated predictive uncertainty, particularly under complex noise structures, but incur substantially higher computational cost. In contrast, variational inference offers competitive predictive performance together with markedly improved computational efficiency, although it may underestimate posterior uncertainty in more challenging settings. Overall, the findings suggest that the primary practical benefit of Bayesian neural networks lies in reliable and interpretable uncertainty quantification rather than solely improved point prediction. The choice of inference strategy should therefore balance posterior fidelity, uncertainty calibration, and computational scalability according to https://jirss.irstat.ir/article_737792.html Non-response and extreme values are two major issues faced by surveyors at the survey and estimation stage; they lead to biased and inefficient estimators. This study proposes four unbiased estimators of the finite population mean in the presence of extreme values when non-response occurs in surveys. We consider two robust ranked set sampling (RSS) designs, called improved paired RSS (IPRSS) and median RSS (MRSS) for the derivation of the proposed estimators. Expressions are provided for the variance of the proposed estimator, and it is mathematically proved that these estimators are more efficient than the Hansen naïve model. Additionally, the efficiency conditions are provided in comparison with the Bouza model of handling non-response in RSS. We conduct a comprehensive simulation study to observe the relative efficiency of the proposed estimators based on hypothetical normal data. For applications in real life, we also consider heart failure data for simulations.