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
