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IRAQI JOURNAL OF STATISTICAL SCIENCES最新文献

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A comparison among robust estimation methods for structural equations modeling with ordinal categorical variables 有序分类变量结构方程建模的鲁棒估计方法比较
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167418
Omar Salim ALheialy, M. J. Mohammed
{"title":"A comparison among robust estimation methods for structural equations modeling with ordinal categorical variables","authors":"Omar Salim ALheialy, M. J. Mohammed","doi":"10.33899/IQJOSS.2020.167418","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167418","url":null,"abstract":"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 chisquare robust, TLI, CFI and RMSIA.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124046975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gene selection in cox regression model based on a new adaptive elastic net penalty 基于自适应弹性网罚的cox回归模型中的基因选择
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167386
Oday Isam Alskal, Z. Algamal
{"title":"Gene selection in cox regression model based on a new adaptive elastic net penalty","authors":"Oday Isam Alskal, Z. Algamal","doi":"10.33899/IQJOSS.2020.167386","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167386","url":null,"abstract":"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.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123619621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Solving a travelling salesman problem with heuristic model approach and comparing with AMPL solution 用启发式模型方法求解旅行商问题，并与AMPL方法进行比较
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167384
Govind Sharma, Amit Kumar
{"title":"Solving a travelling salesman problem with heuristic model approach and comparing with AMPL solution","authors":"Govind Sharma, Amit Kumar","doi":"10.33899/IQJOSS.2020.167384","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167384","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115234085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization 基于自然启发算法优化的Nadaraya-Watson核非参数回归平滑参数选择
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167389
Zinah Ameer Basheer, Z. Algamal
{"title":"Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization","authors":"Zinah Ameer Basheer, Z. Algamal","doi":"10.33899/IQJOSS.2020.167389","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167389","url":null,"abstract":"In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the natureinspired 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.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131119388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Some wavelet filters to estimate non-parametric GAM models with application and simulation 用小波滤波器估计非参数GAM模型，并进行了应用和仿真
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167385
Alaa Abulsattar Hamoodat, B. AL-Talib
{"title":"Some wavelet filters to estimate non-parametric GAM models with application and simulation","authors":"Alaa Abulsattar Hamoodat, B. AL-Talib","doi":"10.33899/IQJOSS.2020.167385","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167385","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114759992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Parameters estimation of homogeneous gamma process via intelligence techniques 基于智能技术的齐次伽马过程参数估计
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167388
Ibtehaj Abdulhammed Algasoo, S. W. Mahmood, Ghalya Tawfeeq Basheer
{"title":"Parameters estimation of homogeneous gamma process via intelligence techniques","authors":"Ibtehaj Abdulhammed Algasoo, S. W. Mahmood, Ghalya Tawfeeq Basheer","doi":"10.33899/IQJOSS.2020.167388","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167388","url":null,"abstract":"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).","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125458361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Use the k nearest neighbor(KNN) to compare the classification of real age and age through the bone for thalassic patients 使用k近邻(KNN)比较地中海患者的真实年龄和通过骨骼的年龄分类
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167392
O. Al-Rawi
{"title":"Use the k nearest neighbor(KNN) to compare the classification of real age and age through the bone for thalassic patients","authors":"O. Al-Rawi","doi":"10.33899/IQJOSS.2020.167392","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167392","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134019547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Employment the black box models to forecast the central bank’s foreign currency sales 使用黑箱模型来预测央行的外汇销售
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167393
A. Hassan, N. S. Ibrahim
{"title":"Employment the black box models to forecast the central bank’s foreign currency sales","authors":"A. Hassan, N. S. Ibrahim","doi":"10.33899/IQJOSS.2020.167393","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167393","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121500773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using ridge regression to analysis the meteorological data in sulaimani 利用脊回归对苏莱曼尼地区气象资料进行分析
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167390
Layla A. Ahmed
{"title":"Using ridge regression to analysis the meteorological data in sulaimani","authors":"Layla A. Ahmed","doi":"10.33899/IQJOSS.2020.167390","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167390","url":null,"abstract":"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.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126507132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Generalized ridge estimator shrinkage estimation based on particle swarm optimization algorithm 基于粒子群优化算法的广义脊估计收缩估计
IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2020-12-01 DOI: 10.33899/IQJOSS.2020.167387
Qamar Abdul Kareem, Z. Algamal
{"title":"Generalized ridge estimator shrinkage estimation based on particle swarm optimization algorithm","authors":"Qamar Abdul Kareem, Z. Algamal","doi":"10.33899/IQJOSS.2020.167387","DOIUrl":"https://doi.org/10.33899/IQJOSS.2020.167387","url":null,"abstract":"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.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131167543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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