IRAQI JOURNAL OF STATISTICAL SCIENCES最新文献

{"title":"New Approach to Approximating the Cumulative Function for the t-Distribution","authors":"Ahmad AL-SHALLAWI","doi":"10.33899/iqjoss.2023.0181184","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.0181184","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"41 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017631","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}

{"title":"Application of Elman Neural Network and SARIMA Model to Modeling Road Traffic Accident in the Kurdistan Region of Iraq","authors":"Saman Mahmood, Mryam Ahmed","doi":"10.33899/iqjoss.2023.0181152","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.0181152","url":null,"abstract":"Abstract","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"89 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139014425","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}

{"title":"Exponential Power-Chen Distribution and Its Some Properties","authors":"Noorsl Zeenalabiden, Buğra Saraçoğlu","doi":"10.33899/iqjoss.2023.181219","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181219","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"72 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138988595","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}

{"title":"Theory-based Model Validation in the Generalized Multifactor Dimensionality Reduction Algorithm for Ordinal Phenotypes  ","authors":"Mohammed Othman, Zaid Al-Khaledi","doi":"10.33899/iqjoss.2023.181255","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181255","url":null,"abstract":"Clinical studies indicate a close relationship between some diseases and the presence of specific interactions between genetic factors. As is the case in many studies, revealing genetic interactions that have a significant impact on the emergence of genetic diseases requires extensive statistical analyses. Because of the enormous volume of genetic data in the human race, it was necessary to develop statistical methods adapted to deal with high-dimensional data. Multifactor Dimensionality Reduction (MDR) is one of the leading nonparametric algorithms in this field. The algorithm reduces the dimensions of genetic data to obtain the most important interaction that has a direct impact on increasing the likelihood of genetic diseases appearing. In its composition, the algorithm relies on a set of nonparametric procedures to diagnose genetic interference with the highest impact exclusively on binary response variables. Like any statistical method, this algorithm is not devoid of weaknesses and application limitations, so the algorithm had to be developed to overcome the obstacles. One of the weaknesses of this algorithm is that the algorithm cannot handle data sets with ordinal response variable. Some researchers have developed a generalization of the multifactor dimensionality reduction algorithm to enable it to work with ordinal data. However, the generalized algorithm is more complex than the original algorithm. Therefore, we proposed developing the original algorithm in a simple way by employing ordinal logistic regression to classify individuals in the sample, while keeping all steps of the original algorithm unchanged. On the other hand, the MDR algorithm adopts a non-parametric method to verify the significance of the interferences nominated in the algorithm. This nonparametric procedure is based on the idea of permutational tests, and it consumes a very long time compared to parametric procedures that relies on theoretical approaches. Some researchers have suggested using the generalized extreme value distribution to verify the statistical significance of candidate interactions, but this method has only been used with continuous and binary dependent variables. In this research, the theoretical method based on the generalized extreme value distribution was employed instead of the permutational tests adopted in the algorithm when the response variable is of the ordinal type.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"299 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139024930","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}

{"title":"Extract Analytical Indicators for Covid 19 Disease Database","authors":"Hayfaa Al-Tai, AmmarT. Al Abd Alazeez Abd Alazeez","doi":"10.33899/iqjoss.2023.181227","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181227","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"197 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138991129","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}

{"title":"Robustifying Cox - Regression Model Estimation Using M - estimators with application to breast cancer patients","authors":"Salwa Qassim Haidari, B. AL-Talib","doi":"10.33899/iqjoss.2023.181221","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181221","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"189 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139014076","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}

{"title":"Using Time-Inhomogeneity Markov Chain For Testing Kidney Diseases Departures: Apply Study For Razgari Hospital in Erbil-Iraq","authors":"Mahdi Raza","doi":"10.33899/iqjoss.2023.181217","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181217","url":null,"abstract":"","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"155 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139024824","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}

{"title":"Analysis of Two Populations Dichotomous Data in Latent Variable Models Using Bayesian Approach","authors":"T. Thanoon, Robiah Adnan, Zarina Khalid","doi":"10.33899/iqjoss.2023.0181176","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.0181176","url":null,"abstract":"Abstract","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"156 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013639","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}

{"title":"The Comparison Between VAR and ARIMAX Time Series Models in Forecasting","authors":"Esraa Haydier, Nasradeen Albarwari, T. Ali","doi":"10.33899/iqjoss.2023.181260","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181260","url":null,"abstract":"Abstract","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"605 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021990","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}

{"title":"Comparing SVR and Random Forest Forecasting based on Autoregressive Time Series with Application","authors":"N. Fadhil, Zinah ALbazzaz","doi":"10.33899/iqjoss.2023.181220","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181220","url":null,"abstract":"The accuracy of forecasting the time series of relative humidity in its maximum and minimum cases is important for controlling environmental impacts, damages and risks. In this study, the support vector regression (SVR) method and the random forest (RF) method will be used, depending on the principle of auto regressive (AR) and the autocorrelation (AC), which is the main characteristic of time series in general. The Lags of original time series will be depended as the explanatory (input) variables while the original series will be as target variable. This structure is fitted with the AC principle because the current observation will be depending on time lags in each time step of time series variable. Comparisons of the forecasting results will be performed by using SVR , RF methods and compared to the classical method of analysing time series which is the integrated autoregressive and moving average (ARIMA) model. The SVR and RF methods were employed due to their importance in improving the forecast results, as they are the ideal solution to the problem of non-linearity of the data, as well as the problem of heterogeneity in the climate data, especially as a result of the fact that they contain many seasonal and periodic compounds, which may lead to inaccurate forecast. The forecast of the time series of relative humidity in its minimum and maximum cases was studied in this study for one of the agricultural meteorological stations in the city of Mosul-Iraq. The results of this study reflected the superiority of both SVR method and RF method compared to the classical method represented by the ARIMA model. The results also included the superiority of the RF method in forecasting the training period compared to the SVR method, which was more balanced despite that, as it superiority the results of ARIMA in forecasting the training period and the testing period, while it was its forecast performance is slightly better than the forecast results of the RF method in the test period.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"118 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139024146","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}