Annals of Data Science最新文献_第5页

Bayesian Analysis of Generalized Inverted Exponential Distribution Based on Generalized Progressive Hybrid Censoring Competing Risks Data 基于竞争风险数据的广义逆指数分布的贝叶斯分析

Annals of Data Science Pub Date : 2023-08-21 DOI: 10.1007/s40745-023-00488-y

Amal S. Hassan, Rana M. Mousa, Mahmoud H. Abu-Moussa

引用次数: 0

Quantitative Analysis of Group for Epidemiology Architectural Approach 流行病学体系结构方法的群体定量分析

Annals of Data Science Pub Date : 2023-08-18 DOI: 10.1007/s40745-023-00493-1

Dephney Mathebula

{"title":"Quantitative Analysis of Group for Epidemiology Architectural Approach","authors":"Dephney Mathebula","doi":"10.1007/s40745-023-00493-1","DOIUrl":"10.1007/s40745-023-00493-1","url":null,"abstract":"<div><p>Epidemiology, the aspect of research focusing on disease modelling is date intensive. Research epidemiologists in different research groups played a key role in developing different data driven model for COVID-19 and monkeypox. The requirement of accessing highly accurate data useful for disease modelling is beneficial but not without having challenges. Currently, the task of data acquisition is executed by select individuals in different research groups. This approach experiences the drawbacks associated with getting permission to access the desired data and inflexibility to change data acquisition goals due to dynamic epidemiological research objectives. The presented research addresses these challenges and proposes the design and use of dynamic intelligent crawlers for acquiring epidemiological data related to a given goal. In addition, the research aims to quantify how the use of computing entities enhances the process of data acquisition in epidemiological related studies. This is done by formulating and investigating the metrics of the data acquisition efficiency and the data analytics efficiency. The use of human assisted crawlers in the global information networks is found to enhance data acquisition efficiency (DAqE) and data analytics efficiency (DAnE). The use of human assisted crawlers in a hybrid configuration outperforms the case where manual research group member efforts are expended enhancing the DAqE and DAnE by up to 35% and 99% on average, respectively.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00493-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44922195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of ( P[Y 具有威布尔边际的应力-强度模型依赖性的$$P[Y<X]$$估计

Annals of Data Science Pub Date : 2023-08-17 DOI: 10.1007/s40745-023-00487-z

Dipak D. Patil, U. V. Naik-Nimbalkar, M. M. Kale

{"title":"Estimation of ( P[Y<X] ) for Dependence of Stress–Strength Models with Weibull Marginals","authors":"Dipak D. Patil, U. V. Naik-Nimbalkar, M. M. Kale","doi":"10.1007/s40745-023-00487-z","DOIUrl":"10.1007/s40745-023-00487-z","url":null,"abstract":"<div><p>The stress–strength model is a basic tool used in evaluating the reliability <span>( R = P(Y < X))</span>. We consider an expression for <i>R</i> where the random variables X and Y denote strength and stress, respectively. The system fails only if the stress exceeds the strength. We aim to study the effect of the dependency between X and Y on <i>R</i>. We assume that X and Y follow Weibull distributions and their dependency is modeled by a copula with the dependency parameter <span>( theta )</span>. We compute <i>R</i> for Farlie–Gumbel–Morgenstern (FGM), Ali–Mikhail–Haq (AMH), Gumbel’s bivariate exponential copulas, and for Gumbel–Hougaard (GH) copula using a Monte-Carlo integration technique. We plot the graph of <i>R</i> versus <span>(theta )</span> to study the effect of dependency on <i>R</i>. We estimate <i>R</i> by plugging in the estimates of the marginal parameters and of <span>( theta )</span> in its expression. The estimates of the marginal parameters are based on the marginal likelihood. The estimates of <span>(theta )</span> are obtained from two different methods; one is based on the conditional likelihood and the other is based on the method of moments using Blomqvist’s beta. Asymptotic distribution of both the estimators of <i>R</i> is obtained. Finally, analysis of real data set is also performed for illustrative purposes.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48578734","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

Modi-Weibull Distribution: Inferential and Simulation Study 莫迪-威布尔分布：推理与仿真研究

Annals of Data Science Pub Date : 2023-08-15 DOI: 10.1007/s40745-023-00491-3

Harshita Kumawat, Kanak Modi, Pankaj Nagar

引用次数: 0

Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values 记录值下物流配送位置和规模参数的收缩估计

Annals of Data Science Pub Date : 2023-08-14 DOI: 10.1007/s40745-023-00492-2

Shubham Gupta, Gajendra K. Vishwakarma, A. M. Elsawah

{"title":"Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values","authors":"Shubham Gupta, Gajendra K. Vishwakarma, A. M. Elsawah","doi":"10.1007/s40745-023-00492-2","DOIUrl":"10.1007/s40745-023-00492-2","url":null,"abstract":"<div><p>Logistic distribution (LogDis) is frequently used in many different applications, such as logistic regression, logit models, classification, neural networks, physical sciences, sports modeling, finance and health and disease studies. For instance, the distribution function of the LogDis has the same functional form as the derivative of the Fermi function that can be used to set the relative weight of various electron energies in their contributions to electron transport. The LogDis has wider tails than a normal distribution (NorDis), so it is more consistent with the underlying data and provides better insight into the likelihood of extreme events. For this reason the United States Chess Federation has switched its formula for calculating chess ratings from the NorDis to the LogDis. The outcomes of many real-life experiments are sequences of record-breaking data sets, where only observations that exceed (or only those that fall below) the current extreme value are recorded. The practice demonstrated that the widely used estimators of the scale and location parameters of logistic record values, such as the best linear unbiased estimators (BLUEs), have some defects. This paper investigates the shrinkage estimators of the location and scale parameters for logistic record values using prior information about their BLUEs. Theoretical and computational justifications for the accuracy and precision of the proposed shrinkage estimators are investigated via their bias and mean square error (MSE), which provide sufficient conditions for improving the proposed shrinkage estimators to get unbiased estimators with minimum MSE. The performance of the proposed shrinkage estimators is compared with the performances of the BLUEs. The results demonstrate that the resulting shrinkage estimators are shown to be remarkably efficient.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46550442","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

Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique 利用Vine Copula和模糊聚类技术改进贝叶斯分类器

Annals of Data Science Pub Date : 2023-08-10 DOI: 10.1007/s40745-023-00490-4

Ha Che-Ngoc, Thao Nguyen-Trang, Hieu Huynh-Van, Tai Vo-Van

引用次数: 0

A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data 一个新的Lindley推广:双峰右偏降水数据下的估算、风险评估与分析

Annals of Data Science Pub Date : 2023-08-08 DOI: 10.1007/s40745-023-00485-1

Majid Hashempour, Morad Alizadeh, Haitham M. Yousof

{"title":"A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data","authors":"Majid Hashempour, Morad Alizadeh, Haitham M. Yousof","doi":"10.1007/s40745-023-00485-1","DOIUrl":"10.1007/s40745-023-00485-1","url":null,"abstract":"<div><p>The objectives of this study are to propose a new two-parameter lifespan distribution and explain some of the most essential properties of that distribution. Through the course of this investigation, we will be able to achieve both of these objectives. For the aim of assessment, research is carried out that makes use of simulation, and for the same reason, a variety of various approaches are studied and taken into account for the purpose of evaluation. Making use of two separate data collections enables an analysis of the adaptability of the suggested distribution to a number of different contexts. The risk exposure in the context of asymmetric bimodal right-skewed precipitation data was further defined by using five essential risk indicators, such as value-at-risk, tail-value-at-risk, tail variance, tail mean–variance, and mean excess loss function. This was done in order to account for the right-skewed distribution of the data. In order to examine the data, several risk indicators were utilized. These risk indicators were used in order to achieve a more in-depth description of the risk exposure that was being faced.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42711550","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

Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors 基于条件指定先验的变点问题的贝叶斯分析

Annals of Data Science Pub Date : 2023-08-08 DOI: 10.1007/s40745-023-00484-2

G. Shahtahmassebi, José María Sarabia

引用次数: 0

Bayesian Learning of Personalized Longitudinal Biomarker Trajectory 个性化纵向生物标志物轨迹的贝叶斯学习

Annals of Data Science Pub Date : 2023-08-01 DOI: 10.1007/s40745-023-00486-0

Shouhao Zhou, Xuelin Huang, Chan Shen, Hagop M. Kantarjian

{"title":"Bayesian Learning of Personalized Longitudinal Biomarker Trajectory","authors":"Shouhao Zhou, Xuelin Huang, Chan Shen, Hagop M. Kantarjian","doi":"10.1007/s40745-023-00486-0","DOIUrl":"10.1007/s40745-023-00486-0","url":null,"abstract":"<div><p>This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46463104","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

Applications of Reliability Test Plan for Logistic Rayleigh Distributed Quality Characteristic 物流瑞利分布质量特性可靠性试验计划的应用

Annals of Data Science Pub Date : 2023-07-19 DOI: 10.1007/s40745-023-00473-5

Mahendra Saha, Harsh Tripathi, Anju Devi, Pratibha Pareek

引用次数: 0