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

 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

In this work, an enhanced Photonic Crystal Fiber (PCF) based on Surface Plasmon Resonance (SPR) sensor using a sided polished structure for the detection of toxic ions Arsenic in water was designed and implemented. The SPR curve can be obtained by polishing the side of the PCF after coating the Au film on the side of the polished area, the SPR curve can be obtained. The proposed sensor has a clear SPR effect, according to the findings of the experiments. The estimated signal to Noise Ratio (SNR), sensitivity (S), resolution (R), and Figures of merit (FOM) are approaching; the SNR is 0.0125, S is 11.11 μm/RIU, the resolution is 1.8x〖10〗^(-4), and the FOM is 13.88 for Single-mode Fiber- Photonic Crystal Fiber- single mode Fiber (SMF-P

This study was performed at Nuclear Radiation Hospital in Baghdad for the period from
January 2011 to May 2011. 44 Blood samples were collected from patients suffered lung and
bladder cancer and 24 samples as healthy control individuals.
Routine liver functions tests were studied by measuring S.GPT, S.GOT and Kidney
function was evaluated by estimation of blood urea and creatinine in serum samples of
individuals studied.
It was observed that the incidence of lung and bladder cancer was higher in males than
females patients ( male 81.82 %, 72.73%, female18 .18%, 27.27% respectively).
Insignificant difference was noted among age of lung and bladder cancer patients
compared with control group. The results

Multilevel models are among the most important models widely used in the application and analysis of data that are characterized by the fact that observations take a hierarchical form, In our research we examined the multilevel logistic regression model (intercept random and slope random model) , here the importance of the research highlights that the usual regression models calculate the total variance of the model and its inability to read variance and variations between levels ,however in the case of multi-level regression models, the calculation of  the total variance is inaccurate and therefore these models calculate the variations for each level of the model, Where the research aims to estimate the parameters of this m

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

Background: The use of antiepileptic drugs (AEDs) during pregnancy warrants several side effects and also deleterious effects on fetal development, the antiepileptic drugs have potential to affect the fetal development throughout the pregnancy although, the majority of infants born to epileptic pregnant women are normal but more expose to the malformations. Aim: The present study aimed to investigate the effect of carbamazepine drug on the kidney development at day 7 postnatally in the Albino Rat (Rattus rattus) as a mammalian model. Material & Methods: 20 healthy pregnant female rats were divided into two groups, 10 pregnant rats in each group; group one served as control group administrated distal water while group two used as experimenta