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

         يواجه المصرف تحولات عديدة أثناء سير عمله ولا سيما قد تحول من مصرف صناعي يسعى الى تحقيق التنمية الصناعية ، من خلال منحه قروض وتسهيلات تنموية وتدعمه الدوله ، الى مصرف شامل يسعى الى تحقيق الربحية في ظل تنويع الأنشطة والخدمات والعمليات الائتمانية.يهدف البحث الى دراسة التحولات التي حدثت في المصرف الصناعي، وتأثير هذا التحول على النشاط الائتماني. وقد استند في ذلك على فرضية رئيسة وهي :-

The repeated measurement design is called a complete randomized block design for repeated measurement when the subject is given the all different treatments , in this case the subject is considered as a block . Many of nonparametric methods were considered like Friedman test (1937) and Koch test(1969) and Kepner&Robinson test(1988) when the assumption of normal distribution of the data is not satisfied .as well as F test  when the assumptions of the analysis of variance is satisfied ,where the observations within blocks are assumed to be equally correlated . The purpose of this paper is to summarize the result of the simulation study for comparing these methods as well as present the suggested

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As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then  these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing.

The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the

In this paper, we will study non parametric model when the response variable have missing data (non response) in observations it under missing mechanisms MCAR, then we suggest Kernel-Based Non-Parametric Single-Imputation instead of missing value and compare it with Nearest Neighbor Imputation by using the simulation about some difference models and with difference cases as the sample size, variance and rate of missing data.      

 Water is an essential aspect of life and important in evolution. Recently the potable water quality topic has received much attention. The study aims to determine drinking water quality in Al-Najaf City by collecting samples throughout Al-Najaf city and comparing the results with the Iraqi guidelines (IQS 417) and World Health Organization (WHO) guidelines, as well as to calculate the WQI. Samples were tested in the laboratory between December 2021 and June 2022. The results showed that multiple parameters exceeded the allowable limits during both testing periods; during winter months, the results of TDS and turbidity exceeded the upper limits in multiple locations. Total hardness values also

This research deals with deliberative intentionality in its two dimensions, deliberative intent, direct, and indirect deliberative intent, and deliberative intentionality assumed great importance to ancient and contemporary scholars. The text, relying on deliberative intent as a methodology concerned with the study of language in usage, and the research focuses on direct deliberative intent through (the intent of acceptability, the intent of the presumption, the intent of the informational, the intent of interaction), and the indirect deliberative intent through (the intentionality of the case, the intent of the metaphor, The intent of the communicative impulse, the intent of the allusion,), and these deliberative elements activate and c

The thermoelectric power as a function of temperature for the Iron-Manganese-Aluminum, Fe-Mn-Al, alloys for manganese concentrations 0.04, 0.06, 0.08, 0.10 and 0.20 have been investigated in the temperature range 300 K. to 500 K. these results showed that the thermoelectric power coefficient in hot probe measurements showed that the electrons are the majority charge carriers in these alloys.