In this article we propose a general Bayesian estimation framework for the Birnbaum-Saunders Quantile Regression (BSQ) model which is used to analyze asymmetric and heterogeneous economic data. In this work, the novel aspect is the joint incorporation of several existing surrogate structures (with different degrees of approximation) and decision-theoretic loss functions (with direct impact on estimation performance trade-offs) under a common BSQ framework conducive to systematic testing of their effect on quantile estimation performance. We consider two previous prior specifications which are Jeffreys prior and weakly informative priors together with three loss functions quadratic, LINEX and the proposed quadratic-logarithmic loss function. Iraqi economic data analysis demonstrates significant disparities in independent variable impacts across quantiles, confirming heterogeneous distribution. The results show further that Jeffreys prior yields more consistent estimates in most quantiles, whereas weakly informative priors give better coverage under the tail. Also, this highlights the importance of choosing a loss function and motivates asymmetric and hybrid loss functions for improved accuracy, especially in extreme quantiles. This contribution expands upon previous works by introducing a new general comparative framework that overextends the potential of Bayesian BSQ modelling, providing robustness and flexibility in such context with significant practical function resulting estimation improves in economic real-world applications.
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In this research work an attempt has been made to investigate about the Robustness of the Bayesian Information criterion to estimate the order of the autoregressive process when the error of this model, Submits to a specific distributions and different cases of the time series on various size of samples by using the simulation, This criterion has been studied by depending on ten distributions, they are (Normal, log-Normal, continues uniform, Gamma , Exponential, Gamble, Cauchy, Poisson, Binomial, Discrete uniform) distributions, and then it has been reached to many collection and recommendations related to this object , when the series residual variable is subject to each ( Poisson , Binomial , Exponential , Dis
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.
In this paper, the process of comparison between the tree regression model and the negative binomial regression. As these models included two types of statistical methods represented by the first type "non parameter statistic" which is the tree regression that aims to divide the data set into subgroups, and the second type is the "parameter statistic" of negative binomial regression, which is usually used when dealing with medical data, especially when dealing with large sample sizes. Comparison of these methods according to the average mean squares error (MSE) and using the simulation of the experiment and taking different sample
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An investigation of the quadrupole deformation of Kr, Sr, Zr, and Mo isotopes has been conducted using the HFB method and SLy4 Skyrme parameterization. The primary role of occupancy of single particle state 2d5/2 in the existence of the weakly bound structure around N=50 is probed. Shell gaps are performed using a few other calculations for the doubly magic number 100Sn using different Skyrme parameterizations. We explore the interplays among neutron pairing strength and neutron density profile in two dimensions, along with the deformations of 100Sn.
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This research discussed, the process of comparison between the regression model of partial least squares and tree regression, where these models included two types of statistical methods represented by the first type "parameter statistics" of the partial least squares, which is adopted when the number of variables is greater than the number of observations and also when the number of observations larger than the number of variables, the second type is the "nonparametric statistic" represented by tree regression, which is the division of data in a hierarchical way. The regression models for the two models were estimated, and then the comparison between them, where the comparison between these methods was according to a Mean Square
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المستخلص:
في هذا البحث , استعملنا طرائق مختلفة لتقدير معلمة القياس للتوزيع الاسي كمقدر الإمكان الأعظم ومقدر العزوم ومقدر بيز في ستة أنواع مختلفة عندما يكون التوزيع الأولي لمعلمة القياس : توزيع لافي (Levy) وتوزيع كامبل من النوع الثاني وتوزيع معكوس مربع كاي وتوزيع معكوس كاما وتوزيع غير الملائم (Improper) وتوزيع
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The process of evaluating data (age and the gender structure) is one of the important factors that help any country to draw plans and programs for the future. Discussed the errors in population data for the census of Iraqi population of 1997. targeted correct and revised to serve the purposes of planning. which will be smoothing the population databy using nonparametric regression estimator (Nadaraya-Watson estimator) This estimator depends on bandwidth (h) which can be calculate it by two ways of using Bayesian method, the first when observations distribution is Lognormal Kernel and the second is when observations distribution is Normal Kernel
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The use of foam electrodes as a cathode has proven its efficiency in wastewater treatment. In this study, methyl orange (MO) was treated by Electro-Fenton technology (EFT) using a copper foam (Cf) as a cathode. EFT was an advanced strategy for MO degradation, which accomplished excellent degradation efficiency (%ReMO) exceeded 98% over 35 min treatment period at prime conditions using 0.124 mM of iron salts (FeSO4.7H2O), 0.3 LPM of air flow, 0.2 mA/cm2 of current density (CD), and initial pH of 3.0. The outcomes showed that the air flow rate had the main impact on the %ReMO. Furthermore, the contribution of anodic oxidation (AO) to dye removal was investigated to distinguish its role relative to the EFT mechanism, revealing that the MO degr
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