In this paper, we investigate the connection between the hierarchical models and the power prior distribution in quantile regression (QReg). Under specific quantile, we develop an expression for the power parameter ( ) to calibrate the power prior distribution for quantile regression to a corresponding hierarchical model. In addition, we estimate the relation between the and the quantile level via hierarchical model. Our proposed methodology is illustrated with real data example.
Background: Osteoarthritis is a chronic pathology of the joints causing disability and morbidity. Diacerein is a disease-modifying agent indicated for osteoarthritis management with enhanced performance and have much lower side effects profile than conventional non-steroidal anti-inflammatory drugs. Oral administration of Diacerein is associated with a laxative effect, thus causing treatment discontinuation. Aim: This study aimed to evaluate the activity of Diacerein novasome-based transdermal gel compared with standard oral treatment in the management of induced osteoarthritis in a rat model. Materials and methods: A single intra-articular injection of monosodium iodoacetate was administered to the left knee joint, resulting in the develop
... Show MoreIn this study, the effect of pumping power on the conversion efficiency of nonlinear crystal (KTP) was investigated using laser pump-power technique. The results showed that the higher the pumping power values, the greater the conversion efficiency (η) and, as the crystal thickness increases within limitations, the energy conversion efficiency increases at delay time of (0.333 ns) and at room temperature. Efficiency of 80% at length of KTP crystal (L = 1.75 X 10-3 m) and Pin = 28MW, and also, compare the experimental results with numerical results by using MATLAB program.
This paper assesses the impact of changes and fluctuations in bank deposits on the money supply in Iraq. Employing the research constructs an Error Correction Model (ECM) using monthly time series data from 2010 to 2015. The analysis begins with the Phillips-Perron unit root test to ascertain the stationarity of the time series and the Engle and Granger cointegration test to examine the existence of a long-term relationship. Nonparametric regression functions are estimated using two methods: Smoothing Spline and M-smoothing. The results indicate that the M-smoothing approach is the most effective, achieving the shortest adjustment period and the highest adjustment ratio for short-term disturbances, thereby facilitating a return
... Show MoreStereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi me
... Show MoreA 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
... Show MoreIn this paper, we investigate the behavior of the bayes estimators, for the scale parameter of the Gompertz distribution under two different loss functions such as, the squared error loss function, the exponential loss function (proposed), based different double prior distributions represented as erlang with inverse levy prior, erlang with non-informative prior, inverse levy with non-informative prior and erlang with chi-square prior.
The simulation method was fulfilled to obtain the results, including the estimated values and the mean square error (MSE) for the scale parameter of the Gompertz distribution, for different cases for the scale parameter of the Gompertz distr
... Show MoreIn this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two different loss functions, the squared error loss function and the modified squared error loss function for comparison purpose. A simulation study has been developed in order to gain an insight into the performance on small, moderate and large samples. The performance of these estimators has been explored numerically under different conditions. The efficiency for the estimators was compared according to the mean square error MSE. The results of comparison by MSE show that the efficiency of B
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