{"title":"Correction: Topp-Leone Cauchy Family of Distributions with Applications in Industrial Engineering","authors":"Mintodê Nicodème Atchadé, Mahoulé Jude Bogninou, Aliou Moussa Djibril, Melchior N’bouké","doi":"10.1007/s44199-023-00069-1","DOIUrl":"https://doi.org/10.1007/s44199-023-00069-1","url":null,"abstract":"","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"41 6","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451138","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}
Abstract The goal of this research is to create a new general family of Topp-Leone distributions called the Topp-Leone Cauchy Family (TLC), which is exceedingly versatile and results from a careful merging of the Topp-Leone and Cauchy distribution families. Some of the new family’s theoretical properties are investigated using specific results on stochastic functions, quantile functions and associated measures, generic moments, probability weighted moments, and Shannon entropy. A parametric statistical model is built from a specific member of the family. The maximum likelihood technique is used to estimate the model’s unknown parameters. Furthermore, to emphasize the new family’s practical potential, we applied our model to two real-world data sets and compared it to existing rival models.
本研究的目标是创建一个新的一般Topp-Leone分布族，称为Topp-Leone Cauchy family (TLC)，它是非常通用的，是Topp-Leone和Cauchy分布族仔细合并的结果。利用随机函数、分位数函数和相关测度、一般矩、概率加权矩和香农熵的具体结果研究了新家族的一些理论性质。参数统计模型是由家族的特定成员构建的。利用极大似然技术对模型的未知参数进行估计。此外，为了强调新家族的实际潜力，我们将我们的模型应用于两个真实世界的数据集，并将其与现有的竞争对手模型进行比较。
{"title":"Topp-Leone Cauchy Family of Distributions with Applications in Industrial Engineering","authors":"Mintodê Nicodème Atchadé, Mahoulé Jude Bogninou, Aliou Moussa Djibril, Melchior N’bouké","doi":"10.1007/s44199-023-00066-4","DOIUrl":"https://doi.org/10.1007/s44199-023-00066-4","url":null,"abstract":"Abstract The goal of this research is to create a new general family of Topp-Leone distributions called the Topp-Leone Cauchy Family (TLC), which is exceedingly versatile and results from a careful merging of the Topp-Leone and Cauchy distribution families. Some of the new family’s theoretical properties are investigated using specific results on stochastic functions, quantile functions and associated measures, generic moments, probability weighted moments, and Shannon entropy. A parametric statistical model is built from a specific member of the family. The maximum likelihood technique is used to estimate the model’s unknown parameters. Furthermore, to emphasize the new family’s practical potential, we applied our model to two real-world data sets and compared it to existing rival models.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"44 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282504","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}
Pub Date : 2023-11-13DOI: 10.1007/s44199-023-00067-3
Hadi Saboori, Mahdi Doostparast
Abstract In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, $$ldots ,$$ …, and k inflated points with probabilities $$w_{0},w_1,ldots ,$$ w0,w1,…, and $$w_{k},$$ wk, respectively. By choosing the appropriate values for the weights $$w_{0},ldots ,w_{k},$$ w0,…,wk, various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero– k -inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results.
摘要在计数数据集中，在标准数据分析模型下，某些点的出现频率可能会超出预期。的确，在许多情况下，零点和其他点的频率往往比泊松的频率高。已在文献中研究了适应现有模型来分析膨胀观测。对膨胀数据建模的一种方法是膨胀分布。在本文中，我们扩展了这个膨胀分布。事实上，如果通货膨胀出现在三个或更多的支撑点，那么以前的模型就不合适了。我们提出了一个基于0、1、$$ldots ,$$…和k个膨胀点的模型，分别具有$$w_{0},w_1,ldots ,$$ w 0、w 1、…和$$w_{k},$$ w k的概率。通过为权重$$w_{0},ldots ,w_{k},$$ w 0，…，w k选择合适的值，可以推导出各种膨胀分布，如0 -膨胀分布，0 - 1 -膨胀分布和0 - k -膨胀分布，作为本文提出的模型的特殊情况。利用所得结果对各种实例和实际数据集进行了分析。
{"title":"Zero to k Inflated Poisson Regression Models with Applications","authors":"Hadi Saboori, Mahdi Doostparast","doi":"10.1007/s44199-023-00067-3","DOIUrl":"https://doi.org/10.1007/s44199-023-00067-3","url":null,"abstract":"Abstract In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, $$ldots ,$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> and k inflated points with probabilities $$w_{0},w_1,ldots ,$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:msub> <mml:mi>w</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>w</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> and $$w_{k},$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:msub> <mml:mi>w</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> respectively. By choosing the appropriate values for the weights $$w_{0},ldots ,w_{k},$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:msub> <mml:mi>w</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>w</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero– k -inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347301","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}
Pub Date : 2023-10-30DOI: 10.1007/s44199-023-00065-5
Zeeshan Basit, Saadia Masood, Ishaq Bhatti
Abstract This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators.
{"title":"A Class of Estimators for Estimation of Population Mean Under Random Non-response in Two Phase Successive Sampling","authors":"Zeeshan Basit, Saadia Masood, Ishaq Bhatti","doi":"10.1007/s44199-023-00065-5","DOIUrl":"https://doi.org/10.1007/s44199-023-00065-5","url":null,"abstract":"Abstract This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104879","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}
Pub Date : 2023-10-19DOI: 10.1007/s44199-023-00064-6
Lovleen Kumar Grover, Anchal Sharma
Abstract The present paper deals with the problem of estimation of finite population mean of study variable using two auxiliary variables in two-phase sampling scheme using predictive approach in case of missing values of the study variable and unknown population mean of first auxiliary variable. Four classes of such estimators have been proposed using this predictive approach. The expressions of bias and mean square errors are derived up to first order of approximation. The optimal values of the constants involved in the proposed classes of estimators have been obtained and thus minimum mean square errors of the proposed classes are obtained in this study. The empirical and graphical comparisons with regression type estimators (under single phase and double phase sampling scheme) and also among themselves have been made for evaluating the performance of the proposed classes for different choices of non-responding units. Five real data sets and three simulated data sets following normal distribution have been used to evaluate the performance of the proposed classes. Numerical findings confirm the theoretical results obtained regarding superiority of proposed classes of estimators over the conventional regression type estimators in terms of percent relative efficiencies.
{"title":"Predictive Estimation of Finite Population Mean in Case of Missing Data Under Two-phase Sampling","authors":"Lovleen Kumar Grover, Anchal Sharma","doi":"10.1007/s44199-023-00064-6","DOIUrl":"https://doi.org/10.1007/s44199-023-00064-6","url":null,"abstract":"Abstract The present paper deals with the problem of estimation of finite population mean of study variable using two auxiliary variables in two-phase sampling scheme using predictive approach in case of missing values of the study variable and unknown population mean of first auxiliary variable. Four classes of such estimators have been proposed using this predictive approach. The expressions of bias and mean square errors are derived up to first order of approximation. The optimal values of the constants involved in the proposed classes of estimators have been obtained and thus minimum mean square errors of the proposed classes are obtained in this study. The empirical and graphical comparisons with regression type estimators (under single phase and double phase sampling scheme) and also among themselves have been made for evaluating the performance of the proposed classes for different choices of non-responding units. Five real data sets and three simulated data sets following normal distribution have been used to evaluate the performance of the proposed classes. Numerical findings confirm the theoretical results obtained regarding superiority of proposed classes of estimators over the conventional regression type estimators in terms of percent relative efficiencies.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135732024","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}
Pub Date : 2023-09-11DOI: 10.1007/s44199-023-00063-7
Aditya Chakraborty, Chris P. Tsokos
Abstract Pancreatic cancer is one of the deadliest carcinogenic diseases affecting people all over the world. The majority of patients are usually detected at Stage III or Stage IV, and the chances of survival are very low once detected at the late stages. This study focuses on building an efficient data-driven analytical predictive model based on the associated risk factors and identifying the most contributing factors influencing the survival times of patients diagnosed with pancreatic cancer using the XGBoost (eXtreme Gradient Boosting) algorithm. The grid-search mechanism was implemented to compute the optimum values of the hyper-parameters of the analytical model by minimizing the root mean square error (RMSE). The optimum hyperparameters of the final analytical model were selected by comparing the values with 243 competing models. To check the validity of the model, we compared the model’s performance with ten deep neural network models, grown sequentially with different activation functions and optimizers. We also constructed an ensemble model using Gradient Boosting Machine (GBM). The proposed XGBoost model outperformed all competing models we considered with regard to root mean square error (RMSE). After developing the model, the individual risk factors were ranked according to their individual contribution to the response predictions, which is extremely important for pancreatic research organizations to spend their resources on the risk factors causing/influencing the particular type of cancer. The three most influencing risk factors affecting the survival of pancreatic cancer patients were found to be the age of the patient, current BMI, and cigarette smoking years with contributing percentages of 35.5%, 24.3%, and 14.93%, respectively. The predictive model is approximately 96.42% accurate in predicting the survival times of the patients diagnosed with pancreatic cancer and performs excellently on test data. The analytical methodology of developing the model can be utilized for prediction purposes. It can be utilized to predict the time to death related to a specific type of cancer, given a set of numeric, and non-numeric features.
{"title":"An AI-driven Predictive Model for Pancreatic Cancer Patients Using Extreme Gradient Boosting","authors":"Aditya Chakraborty, Chris P. Tsokos","doi":"10.1007/s44199-023-00063-7","DOIUrl":"https://doi.org/10.1007/s44199-023-00063-7","url":null,"abstract":"Abstract Pancreatic cancer is one of the deadliest carcinogenic diseases affecting people all over the world. The majority of patients are usually detected at Stage III or Stage IV, and the chances of survival are very low once detected at the late stages. This study focuses on building an efficient data-driven analytical predictive model based on the associated risk factors and identifying the most contributing factors influencing the survival times of patients diagnosed with pancreatic cancer using the XGBoost (eXtreme Gradient Boosting) algorithm. The grid-search mechanism was implemented to compute the optimum values of the hyper-parameters of the analytical model by minimizing the root mean square error (RMSE). The optimum hyperparameters of the final analytical model were selected by comparing the values with 243 competing models. To check the validity of the model, we compared the model’s performance with ten deep neural network models, grown sequentially with different activation functions and optimizers. We also constructed an ensemble model using Gradient Boosting Machine (GBM). The proposed XGBoost model outperformed all competing models we considered with regard to root mean square error (RMSE). After developing the model, the individual risk factors were ranked according to their individual contribution to the response predictions, which is extremely important for pancreatic research organizations to spend their resources on the risk factors causing/influencing the particular type of cancer. The three most influencing risk factors affecting the survival of pancreatic cancer patients were found to be the age of the patient, current BMI, and cigarette smoking years with contributing percentages of 35.5%, 24.3%, and 14.93%, respectively. The predictive model is approximately 96.42% accurate in predicting the survival times of the patients diagnosed with pancreatic cancer and performs excellently on test data. The analytical methodology of developing the model can be utilized for prediction purposes. It can be utilized to predict the time to death related to a specific type of cancer, given a set of numeric, and non-numeric features.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980459","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}
Pub Date : 2023-09-11DOI: 10.1007/s44199-023-00062-8
Lahiru Wickramasinghe, Alexandre Leblanc, Saman Muthukumarana
Abstract When the cells are ordinal in the multinomial distribution, i.e., when cells have a natural ordering, guaranteeing that the borrowing information among neighboring cells makes sense conceptually. In this paper, we introduce a novel probability distribution for borrowing information among neighboring cells in order to provide reliable estimates for cell probabilities. The proposed smoothed Dirichlet distribution forces the probabilities of neighboring cells to be closer to each other than under the standard Dirichlet distribution. Basic properties of the proposed distribution, including normalizing constant, moments, and marginal distributions, are developed. Sample generation of smoothed Dirichlet distribution is discussed using the acceptance-rejection algorithm. We demonstrate the performance of the proposed smoothed Dirichlet distribution using 2018 Major League Baseball (MLB) batters data.
{"title":"Smoothed Dirichlet Distribution","authors":"Lahiru Wickramasinghe, Alexandre Leblanc, Saman Muthukumarana","doi":"10.1007/s44199-023-00062-8","DOIUrl":"https://doi.org/10.1007/s44199-023-00062-8","url":null,"abstract":"Abstract When the cells are ordinal in the multinomial distribution, i.e., when cells have a natural ordering, guaranteeing that the borrowing information among neighboring cells makes sense conceptually. In this paper, we introduce a novel probability distribution for borrowing information among neighboring cells in order to provide reliable estimates for cell probabilities. The proposed smoothed Dirichlet distribution forces the probabilities of neighboring cells to be closer to each other than under the standard Dirichlet distribution. Basic properties of the proposed distribution, including normalizing constant, moments, and marginal distributions, are developed. Sample generation of smoothed Dirichlet distribution is discussed using the acceptance-rejection algorithm. We demonstrate the performance of the proposed smoothed Dirichlet distribution using 2018 Major League Baseball (MLB) batters data.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135936331","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}
Pub Date : 2023-09-04DOI: 10.1007/s44199-023-00061-9
Anupam Pathak, Anoop Chaturvedi, Taruna Kumari
{"title":"Correction: Estimation of Reliability in Multicomponent Set-up when Stress and Strength are Non-identical","authors":"Anupam Pathak, Anoop Chaturvedi, Taruna Kumari","doi":"10.1007/s44199-023-00061-9","DOIUrl":"https://doi.org/10.1007/s44199-023-00061-9","url":null,"abstract":"","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135403882","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}
Pub Date : 2023-07-24DOI: 10.1007/s44199-023-00060-w
Anupam Pathak, A. Chaturvedi, T. Kumari
{"title":"Estimation of Reliability in Multicomponent Set-up when Stress and Strength are Non-identical","authors":"Anupam Pathak, A. Chaturvedi, T. Kumari","doi":"10.1007/s44199-023-00060-w","DOIUrl":"https://doi.org/10.1007/s44199-023-00060-w","url":null,"abstract":"","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"6 1","pages":"213 - 233"},"PeriodicalIF":1.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76422088","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}
Pub Date : 2023-07-22DOI: 10.1007/s44199-023-00059-3
Surajit Bhattacharyya
{"title":"Statistical Quality Control: Acceptance Sampling Plans in the Light of Fuzzy Mathematics","authors":"Surajit Bhattacharyya","doi":"10.1007/s44199-023-00059-3","DOIUrl":"https://doi.org/10.1007/s44199-023-00059-3","url":null,"abstract":"","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"13 1","pages":"170 - 212"},"PeriodicalIF":1.0,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83625717","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}
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