IRAQI JOURNAL OF STATISTICAL SCIENCES

The paper will talk about on the two strategies to take care of the TSP issue of a book shop. The TSP issue arrangement finds the ideal course which advances the course and cost. The paper shows the examination aftereffect of Hungarian strategy hand approach and AMPL program. The client characterized work is associated with AMPL to take care of a progressively muddled issue. This shows the better outcome between the both. The motivation behind the paper is to discover the figuring by AMPL programming and approach for ideal course. The AMPL writing computer programs is utilized for the arrangement of Linear and Non direct conditions. Right now are examining the issue of book retailer who needs to visit the five urban areas to satisfy the interest.
 
 

This study shed light on the method of estimating the GAM based on Smoothing splines repetitive graders . The Wavelet Shrinkage method was used as a paving of data when estimating the GAM by using some wavelets as filters in calculating the wavy intermittent transformation, including (Haar Wavelet, Daubecheis Wavelet, Coiflets Wavelet, wavelet Least Asymmetric) with one of the types of threshold cutting which is Soft Threshold Thresholding to obtain modified coefficients for intermittent wavelet transformation with explanatory and response variables and dependence on estimating the WGAM model with a comparison of the results of all methods with some comparative statistical criteria, And that is by employing the simulation method as well as through real data analysis, and for this purpose data was collected from Ibn Sina Teaching Hospital (Al-Wafa Specialist Center for Diabetes and Endocrinology Consultant Short Stature) for Nineveh Governorate - 2019, for cases of short stature, and a program was used R for the purpose of writing some code for the purpose of obtaining the desired results from this research.

Regression analysis is great of interest in several studies, especially in medicine. The Cox regression model is one of the most important models of regression used in the medical field. It is the tool by which the dependent variable is modeled when the values of that variable are in the form of survival time data. As in linear regression model, the Cox regression model may contain many explanatory variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox regression model is the most popular model in regression analysis for censored survival data. In this paper, a new adaptive elastic net penalty with Cox regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox regression model with the weighted L1-norm. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.

It is well-known that in the presence of multicollinearity, the ridge estimator is an alternative to the ordinary least square (OLS) estimator. Generalized ridge estimator (GRE) is an generalization of the ridge estimator. However, the efficiency of GRE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GRE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error. 

Recently, the Gamma process has been increasing used to model stochastic deterioration for optimizing maintenance because are well suited for modeling the temporal variability of deterioration. In this paper, we discussed two algorithms of the intelligent technique algorithms with moment method for estimating the parameters of the homogeneous gamma process. The application results demonstrate that the intelligent techniques estimation methods are considerably consistent in estimation compared to the moment method, using mean absolute error (MAE).
 

In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this paper, a firefly optimization algorithm method is proposed to choose the smoothing parameter in Nadaraya-Watson kernel nonparametric regression. The proposed method will efficiently help to find the best smoothing parameter with a high prediction. The proposed method is compared with four famous methods. The experimental results comprehensively demonstrate the superiority of the proposed method in terms of prediction capability.
 

Linear regression is one of the frequently used statistical methods that have applications in all field of daily life. In a statistical perspective, the regression analysis is used for studying the relationship between a dependent variable and a set of independent variables. The ridge regression is the most widely model in solving the multicolinearity problem, and it''''s an alternative to OLS.Multicollinearity is the most common problem in multiple regression models in which there exists a perfect relationship between two explanatory variables or more in the model. In this study, ridge regression model was used to estimate linear regression model. This result was compared with result obtained using ordinary least squares model in order to find the best regression model. We have used meteorological data in this study. The results showed that the ridge regression method can be used to resolve the multicollinearity problem, without deleting the independent correlated variables of the model and able to estimate parameters with lower standard error values.
 

In this research, it was suggested to use the InformativeBayes  method in calculating the Bayes weights and use them to treat the of heterogeneity  problem when estimating the linear regression model parameters using the weighted least squares method (BWLS). And compare it with the classical method through an experimental side to simulate the generated data from a normal distribution and for several different cases as well as an applied side of real data. The results of the research provided the preference of the proposed method on the classical method by relying on some statistical criteria through a program designed for this purpose in the language of MATLAB.
 

Thalassemia is considered a chronic disease, especially children from the first years of life, and the patient goes through stages over long periods, Data were collected for patients by real age and age through the bone, Therefore, a comparison will be made between the two cases.
 There are many statistical methods used to arrive at a classification of data, the method of nearest neighbor has been relied upon as a method of classification between societies. The method of classifying each observation depends on the three closest values ​​on the basis of which the observation is placed into the correct group, the naturalness of the data was rather close, so it asked us to use a method that helps us to reach a better classification. The k the nearest neighbor is the best way to reach an optimal classification for such data. Classification by real age was better than classification by bone age using classification. Classification by actual age was better than classification by bone age using k nearest neighbor classification
 

The research is aims to forecasting multi-variable time series using black box models that link the input series with the output series with a mathematical model as it includes two types of models, which are equation error models and output error models, where the model rank was determined using a number of statistical and engineering criteria, namely (AIC, AICC, BIC, LOSS, FPE, FIT) and choosing the model corresponding to the lowest values ​​of the criteria as the best model for forecasting the future values ​​of the Central Bank's sales of foreign currency as a series of outputs and the demand gap as an input series, The results of the analysis showed that the appropriate model for the data is the model ARMAX(1,2,3,1) By relying on the ARMAX model, the central bank’s sales were predicted for the next four months, and the forecast results were consistent with the original time series values, indicating the ARMAX model’s efficiency.
 

Categorical and ordered variables are commonly used in many scientific researches. Researchers often use the ML method, which assumes a multivariate normal distribution, and this is not true with categorical data because the normal state assumption is violated when a Likert scale is used which leads to shaded results.
In this research, it has been suggested the robust MLR method with covariance matrix of the sample which deals with the data as it is a continuous data especially when the Likert scale is five or above.  It has been suggested a method for reducing the error by linking error measurement, where a link was performed between three standard errors, and through the fit  indices, it was obtained a good result in reducing the standard error of capabilities and improving the quality of fit indexes.  It has been also used two of the robust methods, WLSMV method which known as RDWLS method, and ULSMV method which known as RULS method, use a polychoric correlation, each two methods deal with the data as it categorical.    This research also included a comparison between the robust estimation methods ML , MLR , WLSMV and ULSMV and study its effects on the population corrected robust model fit indexes , and then select the best method for dealing with the categorical ordered data . The results showed a superiority of the robust methods in comparison with other methods, where it showed a robust corrections in the standard errors by using the polychoric correlation coefficient matrix, in addition to robust correction of the chi square. In addition of that, the fit indices is replaced by the robust fit indexes of chi- square robust, TLI, CFI and RMSIA.