Iranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601Functional Principal Component Analysis of Intracranial Pressure Data11072551410.22034/jirss.2025.2021358.1050ENArezooOroojiSocial Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.S. Mohammad EHosseini-NasabDepartment of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.HasanDoostiDepartment of Mathematics and Statistics, Macquarie University, 2109 Sydney, Australia.HumainBaharvahdatDepartment of Neurosurgery, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad 99199‑91766, Iran.HabibollahEsmailySocial Determinates of Health Research Center, Mashhad University of Medical Sciences, Mashhad, IranJournal Article20240211The term "functional data" refers to data where the units of observation are functions defined over a time interval. The fundamental philosophy behind functional data is that the repeated measurements for each individual are considered as a stochastic process over time. One of the commonly used analyses for such data is functional principal component analysis. In this study, since the intracranial pressure is measured over time in patients with subarachnoid hemorrhage due to aneurysm, functional principal component analysis is employed to identify the main factors contributing to increased intracranial pressure. The first four functional principal components account for 87.8 percent of the total variation in the intracranial pressure curve. The first, second, third, and fourth principal components explain approximately 52.3, 21.9, 8, and 5.6 percent of the overall variation, respectively. These four components are linked to the total Glasgow Coma Scale score, diastolic blood pressure, age, and systolic blood pressure, respectively.https://jirss.irstat.ir/article_725514_18c2ca91223dda381f6f8aef016e3f43.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601New Estimations for Varextropy under Complete Data and Uniformity Testing113472551510.22034/jirss.2025.2049691.1092ENFaranakGoodarziFaculty of Mathematics, Department of Statistics, University of KashanRahelehZaminiDepartment of Mathematics, Faculty of Mathematical Sciences and Computer,
Kharazmi University, Tehran, Iran.Journal Article20250103Recently Alizadeh Noughabi and Shafaei Noughabi (2024) introduced some estimators for the varextropy of an absolutely continuous random variable. In this paper, we propose other nonparametric estimators for the varextropy function. Additionally, we prove asymptotic properties of two estimators given in Alizadeh Noughabi<br>and Shafaei Noughabi (2024). Asymptotic properties of the proposed estimators are established under suitable regularity conditions. Moreover, a simulation study is performed to compare the performance of the proposed estimators based on mean squared error (MSE) and bias. Furthermore, by using the proposed estimators some tests are constructed for uniformity. It is shown that the varextropy-based test proposed in this paper performs well in terms of power when compared to other uniformity hypothesis tests. Real datasets are utilized to evaluate the performance of the varextropy estimators.https://jirss.irstat.ir/article_725515_68bc055081f79587509953218a88dd0f.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601A Model for Determining Insured Premiums Based on Household Expenses Using Advanced Computational Techniques under Heterogeneous Data Conditions356673177010.22034/jirss.2025.2033194.1067ENAmirAkbarzadeh JanatabadDepartment of Industrial Engineering, Yazd University, IranAhmadSadegheihDepartment of Industrial Engineering, Yazd University, IranM MLotfiDepartment of Industrial Engineering, Yazd University, IranJournal Article20240620The escalating public health costs are a significant concern for governments globally. The efficient management of those costs is critical, with health insurance systems playing a pivotal role. However, the insurance industry faces challenges due to the heterogeneous data, leading to inconsistent outputs for identical inputs. Traditional predictive methods such as Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) often fail to address these inconsistencies. This study proposes a novel two-stage model to determine insurance premiums, incorporating equity considerations and advanced computational techniques. We advocate for an expenditure-based premium calculation as a superior alternative to the traditional salary-based approach. This method aligns premiums more closely with household expenses, promoting fairness and efficiency. Our results demonstrate that the expenditure-based strategy outperforms the salary-based one in controlling costs for both the insured and the insurer. Specifically, the error metrics, including Mean Absolute Error and Root Mean Square Error, show significant improvement in our model compared to the ANFIS method. To enhance the model's accuracy, we integrate sampling techniques to mitigate the data heterogeneity and employ genetic algorithms to optimize the weights of the neural network. The genetic algorithm iteratively evolves the network parameters, ensuring robust performance even in diverse data. Our findings indicate that this integrated approach significantly reduces prediction errors and enhances the overall reliability of the premium calculation process. In conclusion, the proposed model offers a robust framework for premium determination, addressing the inherent data heterogeneity in the insurance industry. This study provides a valuable contribution to the field by demonstrating a practical and effective solution for improving the accuracy and fairness of insurance premium calculations.https://jirss.irstat.ir/article_731770_d7958364f9b2389a7d9d1372ca5ab51d.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601Comparing Linear and non-linear Mixed Effects Models with Autoregressive Error under Small Area Estimation679673177210.22034/jirss.2025.2046692.1086ENAbdolrezaSayyarehDepartment of Computer Sciences and Statistics, Faculty of Mathematics, K.N. Toosi, University of Technology, Tehran, Iran.SedighehZamani-MehreyanDepartment of Statistics, Imam Khomeini International University, Qazvin, IranOmolbaninBashiri GoudarziDepartment of Computer Sciences and Statistics, Faculty of Mathematics, K.N. Toosi, University of Technology, Tehran, Iran.MahmoudTorabiDepartment of Biostatistics, Manitoba University, Winnipeg, CanadaJournal Article20241124The problem of small area estimation is how to produce reliable estimates of characteristics of interest such as means, counts and quantiles. It is usually assumed that the observed values and the auxiliary values follow the linear regression model and the sampling errors are dependent and follow the autoregressive model. However, in practice, there are many situations in the observed values and the auxiliary values follow the non-linear regression model. We assume that the true model is unknown and consider some non-nested, non-linear or linear regression models as rival models and select an optimal model based on extensions of the model selection tests such as Vuong's test. This paper considers non-linear regression models to improve estimation and model selection based on latent variables and proposes a global model selection test for small-area estimation. A numerical example and real data analysis were carried out to illustrate the procedures obtained theoretically.https://jirss.irstat.ir/article_731772_0bd506a52b650ea94eef1999a2733717.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601Generalized Skewed Linnik Distribution and its Application in Time Series9711573180710.22034/jirss.2025.2052626.1100ENBahramTaramiDepartment of Statistics, Science college, Shiraz University, Shiraz, Iran.0000-0002-2222-8853AminShirvaniDepartment of water engineering, Faculty of Agriculture, Shiraz university, Shiraz, IranZahraSajjadniaDepartments of Statistics, Shiraz University, Shiraz, Iran.Journal Article20250204<span class="fontstyle0">Heavy-tailed distributions have recently gained prominence in science, economics, and industry as robust alternatives to the Gaussian distribution, particularly for modeling data with extreme variability or outliers. Several studies in the literature have introduced and examined the Pakes generalized Linnik distribution and its related distributions due to their flexibility in capturing heavy-tailed behavior. However, most existing works focus exclusively on the special case of symmetric random variables—a significant limitation, given that real-world data often exhibit skewness. To address this gap, this paper proposes a new class of generalized skewed Linnik distributions, extending previous symmetric models to accommodate asymmetric data structures. We investigate their theoretical properties, including moments, tail behavior, and stability under linear transformations. Furthermore, we develop an autoregressive (AR) model based on this framework, enabling time-series analysis with skewed, heavytailed innovations. Additionally, we introduce a novel class of geometric skewed Linnik distributions, which arise as the limit of random sums and exhibit unique dependence structures. The practical utility of these models is demonstrated through theoretical derivations and potential applications in finance, risk assessment, and signal processing. Our results broaden the scope of Linnik-based models, o</span><span class="fontstyle2">ff</span><span class="fontstyle0">ering more accurate tools for skewed, heavy-tailed data analysis.</span> https://jirss.irstat.ir/article_731807_4142af5e441c81d0dec7dffc3ffe3f5c.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601Optimal Risk Management Strategies in a General Perturbed Risk Process11713873160910.22034/jirss.2025.2063080.1115ENAbouzarBazyariDepartment of Statistics, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran.0000-0002-7322-9901Journal Article20250608This paper investigates the optimal risk management strategies in a general compound Poisson risk model consisting of safety loading of insurer and reinsurance to minimize the infinite-time ruin probability. Its price process is perturbed by a geometric Brownian motion with the drift and volatility of risky asset. In addition, we allow this company to buy proportional reinsurance to reduce the underlying risk and invest its surplus in a risky asset whose price is driven by correlated Brownian motions. We focus on the possibility of an insurance company utilizing optimal controls and study the optimization problem of minimizing the infinite-time ruin probability in a financial market. For the diffusion approximation of risk model, we obtain an analytic expression for the minimum nfinite-time ruin probability and the corresponding optimal controls by using the martingale approach. Since it is not easy to derive the explicit expression for the infinite-time ruin probability of perturbed risk model, we obtain the optimal strategies to maximize the Lundberg exponent when the claim amounts are identically distributed and have an exponentially decaying tail. Moreover, we study the effect of investment on the ruin probability in both perturbed risk models. Finally, some numerical examples are conducted to illustrate the effects of model parameters on the optimal risk management strategies and on the financial market.https://jirss.irstat.ir/article_731609_b4d99966aaed7ab15086cf78472d636e.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601A Double Multivariate Homogeneously Weighted Moving Average Control Chart: An Extension Work13915673236310.22034/jirss.2025.2013301.1039ENSamson OfformaUgwuDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Akaninyene U.UdomDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Everestus O.OssaiDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Uchenna C.NdukaDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Journal Article20231010This work extends an existing multivariate homogeneously weighted moving average (MHWMA)-control chart to a multivariate double homogeneously weighted moving average (MDHWMA)-control chart aimed at a more efficient monitoring of the process mean vector. Like the MHWMA-control chart, the MDHWMA-control chart statistic assigns a specific weight to the current observation, and the remaining weight is evenly assigned among the previous observations but unlike the MHWMA-control chart, the MDHWMA-control chart statistic utilizes the information contained in the observations twice. We present the design structure of the MDHWMA-control chart and on the basis of the average, standard deviation of the average and the median run lengths (ARL, SDRL & MRL) compare the performance with the MHWMA-control chart in relation to Hotelling's \chi^{2}-chart, multivariate cumulative sum (MCUSUM)-chart and the multivariate exponentially moving average (MEWMA)-chart. The comparison showed that the proposed (MDHWMA)-control chart has a better performance than the competing charts, especially for small shiftshttps://jirss.irstat.ir/article_732363_3f5c134bc4ff99043184922e166d13e2.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724120250601Evaluation of Second-Order Response Designs with Errors in Factor Levels in the Cuboidal Region15716973236210.22034/jirss.2025.2049136.1090ENIfeoma OgochukwuUdeDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.0009-0003-9841-1122Abimibola VictoriaOladugbaDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Amarachi FavourDidiugwuDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Lilian IfeomaNnannaDepartment of Statistics, University of Nigeria, Nsukka, Enugu State, Nigeria.Department of Statistics, University of Regina, Regina, Canada.Journal Article20241228Errors in factor levels often occur in response surface modeling. A design in which these errors have minimal effect is the desired design. <span class="fontstyle0">This study evaluates the prediction capability of second-order Orthogonal Array Composite Design (OACD) and Orthogonal Uniform Composite Design (OUCD) with and without errors in factor levels for</span> 3 ≤ k ≤ 5 factors using 2, 3, and 5 center points in the cuboidal region. Design optimality criteria (in terms of G- and IV-optimality values) and quantile dispersion plots are used to examine the prediction capability of these designs. The results show that OUCD is the preferred design in terms of G-optimality, while IV-optimality and quantile plots indicate that OACD is the preferred design in both the presence and absence of errors in factor levels.https://jirss.irstat.ir/article_732362_ea90131db44cc96d2977f81dac46bf60.pdf