Semiparametric methods combined parametric methods and nonparametric methods ,it is important in most of studies which take in it's nature more progress in the procedure of accurate statistical analysis which aim getting estimators efficient, the partial linear regression model is considered the most popular type of semiparametric models, which consisted of parametric component and nonparametric component in order to estimate the parametric component that have certain properties depend on the assumptions concerning the parametric component, where the absence of assumptions, parametric component will have several problems for example multicollinearity means (explanatory variables are interrelated to each other) , To treat this problem we use
... Show MoreIn some cases, researchers need to know the causal effect of the treatment in order to know the extent of the effect of the treatment on the sample in order to continue to give the treatment or stop the treatment because it is of no use. The local weighted least squares method was used to estimate the parameters of the fuzzy regression discontinuous model, and the local polynomial method was used to estimate the bandwidth. Data were generated with sample sizes (75,100,125,150 ) in repetition 1000. An experiment was conducted at the Innovation Institute for remedial lessons in 2021 for 72 students participating in the institute and data collection. Those who used the treatment had an increase in their score after
... Show MoreThe use of non-parametric models and subsequent estimation methods requires that many of the initial conditions that must be met to represent those models of society under study are appropriate, prompting researchers to look for more flexible models, which are represented by non-parametric models
In this study, the most important and most widespread estimations of the estimation of the nonlinear regression function were investigated using Nadaraya-Watson and Regression Local Ploynomial, which are one of the types of non-linear
... Show MoreIn this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in
Introduction: The study was intended for Roseomonas gilardii NTCC 13290 strain pigment extraction and characterization. Methodology: The pigment-producing bacterial were cultured on Columbia blood agar and nutrient media agar. Then the pigments were extracted by ethanol. The candidate pigment was further characterized by different biotechnological techniques: UV-Vis spectroscopy, FT-IR to analyze the functional group of the targeted pigment, and TLC media. Results: The cultivation of Roseomonas gilardii on media showed pink color and nearly runny texture. The bacterial colonies were microscopically gram stained and examined, the R. gilardii was seen as coccobacillus colonies that mostly form pairs arranged as short chains. The R. gilardii b
... Show MoreIn this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.
This research presents a study in ultra-desulfurization of diesel fuel produced from conventional hydro desulfurization process, using oxidation and solvent extraction techniques. Dibenzothiophene (DBT) was the organosulfur compound that had been detected in sulfur removal. The oxidation process used hydrogen peroxide as an oxidant and acetic acid as homogeneous catalyst . The solvent extraction process used acetonitrile (ACN) and N-methyl – 2 - pyrrolidone (NMP) as extractants . Also the effect of five parameters (stirring speed :150 , 250 , 350 , and 450) rpm, temperature (30 , 40 , 45 , and 50) oC, oxidant/simulated diesel fuel ratio (0.5 , 0.75 , 1 , and 1.5) , catalyst/oxidant ratio(0.125,0.25,0.5
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