Statistical Simulation
Shaghayegh Molaei; Kianoush Fathi Vajargah; Hamid Mottaghi Golshan
Abstract
This article examines the probability structure and dependency structure of a new family of Archimedean copula functions that are generated with two generators; this family is known as a generalization of the Archimedean copula functions and provides more tail dependence properties than the Archimedean ...
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This article examines the probability structure and dependency structure of a new family of Archimedean copula functions that are generated with two generators; this family is known as a generalization of the Archimedean copula functions and provides more tail dependence properties than the Archimedean family, making it more applicable. Using simulations, we compare a member of this family with various existing copula functions to highlight similarities and differences, and if the desired copula's scatter plot in terms of tail dependence is similar to the generalized Archimedean copula, we can fit the generalized Archimedean copula function to it.\\Applications of this copula in the financial domain are demonstrated to improve the study of the dependence between indicators and to use this copula's advantageous characteristics. These theoretical concepts are validated by the numerical example provided at the end of the paper.This article examines the probability structure and dependency structure of a new family of Archimedean copula functions that are generated with two generators; this family is known as a generalization of the Archimedean copula functions and provides more tail dependence properties than the Archimedean family, making it more applicable. Using simulations, we compare a member of this family with various existing copula functions to highlight similarities and differences, and if the desired copula's scatter plot in terms of tail dependence is similar to the generalized Archimedean copula, we can fit the generalized Archimedean copula function to it.\\
Statistical Simulation
fazel tamoradi
Abstract
Economic institutions operating in capital markets face a variety of risks that, if not effectively managed, can lead to financial instability or bankruptcy. This study explores how regulatory and environmental factors influence enterprise risk management (ERM) and its resulting financial outcomes. The ...
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Economic institutions operating in capital markets face a variety of risks that, if not effectively managed, can lead to financial instability or bankruptcy. This study explores how regulatory and environmental factors influence enterprise risk management (ERM) and its resulting financial outcomes. The objective is to identify key elements that enhance ERM effectiveness and assess their impact on financial performance indicators such as financial ratios, market share, and corporate social responsibility. Using a descriptive-survey methodology, the study gathers quantitative data from financial managers and accounting heads in Iranian capital market companies. A structured questionnaire consisting of 54 items on a 5-point Likert scale was developed and validated through expert review and exploratory factor analysis. Random sampling and Cochran’s formula determined a sufficient sample size of 384, though 475 questionnaires were distributed, and 405 valid responses were analyzed. Beyond its descriptive focus, the research adopts a descriptive-correlational approach to examine relationships among risk-related variables. Structural equation modeling (SEM) was used to test hypotheses and evaluate the simultaneous effects of various components—such as information environment quality, competitive strategy, corporate governance, internal control, and management structure—on financial performance outcomes. The results show that these regulatory and environmental factors significantly contribute to strengthening ERM practices, leading to greater financial stability and operational efficiency. The study provides practical insights for investors, managers, and policymakers, demonstrating how external influences can shape robust risk management frameworks and improve financial performance. It also enriches the risk management literature by emphasizing the complex, multidimensional nature of enterprise risks.
Statistical Simulation
Vadood keramati; Ramin Sadeghian; Maryam Hamedi; Ashkan Shabbak
Abstract
Record linkage is a tool used to gather information and data from different sources. It is used in activities related to government, such as e-government and the production of register-based data. This method compares the strings in the databases and there are different methods for record linkage, such ...
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Record linkage is a tool used to gather information and data from different sources. It is used in activities related to government, such as e-government and the production of register-based data. This method compares the strings in the databases and there are different methods for record linkage, such as deterministic and probabilistic assumption. This paper presents a proposed expert system for record linkage of data received from multiple databases. The system is designed to save time and reduce errors in the process of aggregating data. The inputs for this system include several linked fields, thresholds, and metric methods, which are explained along with the evaluation of the used method. To validate the system, inputs from two databases and seven information fields, comprising 100,000 simulated records, were used. The results reveal a higher accuracy of possible record linkage compared to deterministic records. Furthermore, the highest linkage was achieved using five fields with varying thresholds. In assessing the various metric methods, it was found that metric methods with less than 80% accuracy and the Winkler metric method with over 86% accuracy were utilized. These findings demonstrate that the implementation of the proposed automated system significantly saves time and enhances the flexibility of selection methods.
Statistical Simulation
S.M.T. K. MirMostafaee
Abstract
In this paper, we introduce a new extension of the XLindley distribution, called the exponentiated new XLindley distribution. The new model has an increasing or bathtub-shaped hazard rate function, making it suitable for modeling real-life phenomena. We study important properties of the ...
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In this paper, we introduce a new extension of the XLindley distribution, called the exponentiated new XLindley distribution. The new model has an increasing or bathtub-shaped hazard rate function, making it suitable for modeling real-life phenomena. We study important properties of the new model, such as the moments, moment generating function, incomplete moments, mean deviations from the mean and the median, Bonferroni and Lorenz curves, mean residual life function, Rényi entropy, order statistics, and k-record values. We also address the estimation of parameters using the maximum likelihood and bootstrap methods. A Monte Carlo simulation study is conducted to evaluate the estimators discussed in the paper. Additionally, we analyze two real data applications, including rainfall and COVID-19 data sets, to demonstrate the applicability and flexibility of the new distribution. Our results show that the new model fits the data sets better than several other recognized or recently introduced distributions, based on some well-known goodness-of-fit criteria.
Statistical Simulation
Zahra Zandi; Hossein Bevrani; Reza Arabi Belaghi
Abstract
In this paper, we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage, {color{blue} preliminary test}, shrinkage {color{blue} ...
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In this paper, we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage, {color{blue} preliminary test}, shrinkage {color{blue} preliminary test}, shrinkage, and positive shrinkage estimators along with the unrestricted maximum likelihood and restricted estimators. The random effects are considered as nuisance parameters. We conduct a Monte Carlo simulation study to evaluate the performance of each estimator in the sense of simulated relative efficiency. The results of simulation study reveal that the proposed estimation strategies perform more better than {color{blue} the} maximum likelihood method. The proposed estimators are applied to a real dataset to appraise their performance.
Statistical Simulation
farzad eskandari
Abstract
Imprecise measurement tools produce imprecise data. Interval-valued data is usually used to deal with such imprecision. So interval-valued variables are used in estimation methods. They have recently been modeled by linear regression models. If response variable has any statistical distributions, interval-valued ...
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Imprecise measurement tools produce imprecise data. Interval-valued data is usually used to deal with such imprecision. So interval-valued variables are used in estimation methods. They have recently been modeled by linear regression models. If response variable has any statistical distributions, interval-valued variables are modeled in generalized linear models framework. In this article, we propose a new consistent estimator of a parameter in generalized linear models with regard to distributions of response variable in the exponential family. A simulation study shows that the new estimator is better than others on the basis of particular distributions of response variable. We present optimal properties of the estimators in this research
