Analyzing big data sets by using different panelized regression methods with application: Surveys of multidimensional poverty in Iraq

Poverty phenomenon is very substantial topic that determines the future of societies and governments and the way that they deals with education, health and economy. Sometimes poverty takes multidimensional trends through education and health. The research aims at studying multidimensional poverty in Iraq by using panelized regression methods, to analyze Big Data sets from demographical surveys collected by the Central Statistical Organization in Iraq. We choose classical penalized regression method represented by The Ridge Regression, Moreover; we choose another penalized method which is the Smooth Integration of Counting and Absolute Deviation (SICA) to analyze Big Data sets related to the different poverty forms in Iraq. Euclidian Distance (ED) was used to compare the two methods and the research conclude that the SICA method is better than Ridge estimator with Big Data conditions

Bayesian and non-Bayesian estimation of the Lomax model based on upper record values under weighted LINEX loss function

In this article, we developed a new loss function, as the simplification of linear exponential loss function (LINEX) by weighting LINEX function. We derive a scale parameter, reliability and the hazard functions in accordance with upper record values of the Lomax distribution (LD). To study a small sample behavior performance of the proposed loss function using a Monte Carlo simulation, we make a comparison among maximum likelihood estimator, Bayesian estimator by means of LINEX loss function and Bayesian estimator using square error loss (SE) function. The consequences have shown that a modified method is the finest for valuing a scale parameter, reliability and hazard functions

Comparison between VG-levy and Kernel function estimation with application

In this article, we present the variance Gamma Levy model that was obtained from the Brownian motion Gamma with their parameter estimation methods (Maximum Likelihood (MLE) and method of moments (MME)). Then, we compare them with Kernel density function depending on MASE. Our application concerned with the Apple company that is listed on the Nasdaq, their data are suitable for the VG-Levy model and achieved the proper conditions of Levy of stability and independence, which means that Apple company was efficient in providing the information to investors. The aim of studying the price fluctuations through the parameters of the model and thus the possibility of knowing the trends of stock prices in the financial markets and the consequent risks associated with investing in them in a manner consistent with the investor's preferences regarding bearing a certain degree of risk in the sense of a pre-preparedness for the potential sacrifice of the investor's capital with the limits of risk resulting from those fluctuations in prices for the returns resulting from price movements in those markets., So it was found that the VG-levy model with MLE is the best