IRAOI JOURNAL OF STATISTICAL SCIENCES

The paper will focus 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 we are examining the issue of book retailer who needs to visit the five urban areas to satisfy the interest

In this paper, the problem of the unknown probabilistic distribution of data and employing the method of estimating the Non-parametric Generalized Additive Models (GAM) based on smoothing splines as smoothers, and dealing with this case by using an iterative approach. The Wavelet Shrinkage technique was used and relied upon to estimate the Wavelet Generalized Additive Models (WGAM) regression model, which was proposed to be used as a data smoothers through the use of some wavelets as filters in the calculation of the discrete wavelet transformation, and some statistical criteria were used to compare the estimation methods. This done through the use of simulation and analysis of real data as well, the efficiency of the proposed methods was tested on data collected from Ibn Sina Teaching Hospital, on cases of short stature, and wavelet shrinkage smoothers gave the best results compared to the usual GAM method and the wavelet helped on smoothing data, by obtaining the most efficient 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. The generalized ridge estimator (GRE) is a generalization of the ridge estimator. However, the efficiency of the 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 Informative Bayes 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

Use The K Nearest Neighbor(KNN) To Compare The Classification Of Real Age And Age Through The Bone For Thalassic Patients

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.