Pareto distribution is used in many economic, financial and social applications. This distribution is used for the study of income and wealth and the study of settlement in cities and villages and the study of the sizes of oil wells as well as in the field of communication through the speed of downloading files from the Internet according to their sizes. This distribution is used in mechanical engineering as one of the distributions of models of failure, stress and durability. Given the practical importance of this distribution on the one hand, and the scarcity of sources and statistical research that deal with it, this research touched on some statistical characteristics such as derivation of its mathematical function , probability density function, cumulative distribution function, methods of estimating parameters, and the difficulties that researchers may face in dealing with these phenomena. The parameters were estimated in a number of methods, including the Maximum Likelihood (MLE), Ordinary Least Squares (OLS), Moment method (MOM), Relative Least squares (RELS) and Ridge regression (RR). In addition an algorithm has been proposed to improve the estimation parameters for this distribution. MSE was used to determine the best of these methods. Conclusions were presented in the light of this and appropriate proposals were decided upon.
A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others
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This study was aimed to estimate off-farm labor supply model. Data were collected randomly from 267 wheat producers in Salah El-Din province for the year 2020, 67.4% of them are produced using pivot sprinklers for irrigation and with three tenure categories (60,80,12) dunums. Furthermore. The KS coefficient was used to analyze the producers' risk-taking behavior after estimating the production function and determining the area variable that has the most influence on the production process. If it increased by 1%, production would increase by 0.802%. The farmer's decision to adopt the technology was based on economic, social, and institutional factors. It turns out that 40% of farmers make their decision based on financing. When a
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A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the
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Some experiments need to know the extent of their usefulness to continue providing them or not. This is done through the fuzzy regression discontinuous model, where the Epanechnikov Kernel and Triangular Kernel were used to estimate the model by generating data from the Monte Carlo experiment and comparing the results obtained. It was found that the. Epanechnikov Kernel has a least mean squared error.
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