Modeling income distribution: An econophysics approach

This study aims to develop appropriate models for income distribution in Iran using the econophysics approach for the 2006–2018 period. For this purpose, the three improved distributions of the Pareto, Lognormal, and Gibbs-Boltzmann distributions are analyzed with the data extracted from the target household income expansion plan of the statistical centers in Iran. The research results indicate that the income distribution in Iran does not follow the Pareto and Lognormal distributions in most of the study years but follows the generalized Gibbs-Boltzmann distribution function in all study years. According to the results, the generalized Gibbs-Boltzmann distribution also properly fits the actual data distribution and could clearly explain the income distribution in Iran. The generalized Gibbs-Boltzmann distribution also fits the actual income data better than both Pareto and Lognormal distributions

Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling

CC BY 4.0In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size

Turkish version of the test your memory (Tym-tr) as a screening tool in memory clinics

Introduction: This study compared the Turkish version of Test Your Memory (TYM) MMSE (Mini-Mental State Examination) and CDT (Clock Drawing Test) in patients with neurocognitive disorder. Methods: After a thorough medical workup, patients with a diagnosis of neurocognitive disorder were enrolled. A cross-sectional design was used to compare the TYM results with those of MMSE and CDT. Results: This study was conducted on 100 patients, including 46 males and 54 females, aged 52 to 86. The majority of patients were diagnosed with vascular neurocognitive disorder. The z-score of TYM-TR was significantly lower in the domains of registration, recall, visuospatial, and total score. The same results were achieved when CDT was added to MMSE. The same pattern was observed separately for those who were diagnosed with a mild or major neurocognitive disorder. Conclusion: Patients’ cognitive deficits might be more evident when measured by the TYM-TR compared to the MMSE

Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling

In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size.CC BY 4.0Correspondence: [email protected]: This research received no external funding</p