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.
The polymer was used to inhibit the corrosion of copper metal in salt media in different concentrations at room temperature using potentiometric polarization measurement. The polymer was prepared by mixing (0.1 M) 4-Hydroxy aniline (C6H7NO) with (0.25M) of ammonium persulfate as the initiator using the electro-deposition technique. The polymer’s results showed that copper in (3.5%) NaCl had good corrosion resistance. The findings demonstrate that the %IE for polymer-induced copper corrosion is 89.32% at 10 ppm concentration as a result of the 4-hydroxy aniline polymer’s adsorption from salt solution on the surface of copper metal. The numbers from the polarization method and the acquired standard data agree well. The coated copper by po
... Show MoreThe present study focuses on synthesizing solar selective absorber thin films, combining nanostructured, binary transition metal spinel features and a composite oxide of Co and Ni. Single-layered designs of crystalline spinel-type oxides using a facile, easy and relatively cost-effective wet chemical spray pyrolysis method were prepared with a crystalline structure of MxCo3−xO4. The role of the annealing temperature on the solar selective performance of nickel-cobalt oxide thin films (∼725 ± 20 nm thick) was investigated. XRD analysis confirmed the formation of high crystalline quality thin films with a crystallite si
Abstract
Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model
In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe
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People are supposed to use language harmoniously and compatibly. However, aggression may characterize much of human communication. Aggression has long been recognized as a negative anti-social issue that prevails in most personal interactions. If it abounds in familial communications, it is more dangerous due to its harmful effects on individuals, and consequently on societies. Aggression refers to all the instances in which we try to get our way without any consideration for others. Moriarty’s novel (2014), Big Little Lies, is argued to represent the patterns of aggressive communications. This study aims to find out the motivations behind aggressive language in familial communication in this
... Show MoreThe internet has been a source of medical information, it has been used for online medical consultation (OMC). OMC is now offered by many providers internationally with diverse models and features. In OMC, consultations and treatments are available 24/7. The covid-19 pandemic across-the-board, many people unable to go to hospital or clinic because the spread of the virus. This paper tried to answer two research questions. The first one on how the OMC can help the patients during covid-19 pandemic. A literature review was conducted to answer the first research question. The second one on how to develop system in OMC related to covid-19 pandemic. The system was developed by Visual Studio 2019 using software object-oriented approach. O
... Show MoreComputer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a cruc
... Show MoreAbstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization
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