Journal of Statistical Theory and Applications最新文献

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}