Annals of Data Science最新文献_第9页

Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations 为具有强相关性的高维基因组数据选择稳定的变量

Annals of Data Science Pub Date : 2023-06-29 DOI: 10.1007/s40745-023-00481-5

Reetika Sarkar, Sithija Manage, Xiaoli Gao

{"title":"Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations","authors":"Reetika Sarkar, Sithija Manage, Xiaoli Gao","doi":"10.1007/s40745-023-00481-5","DOIUrl":"10.1007/s40745-023-00481-5","url":null,"abstract":"<div><p>High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including the Lasso and MCP, etc. In this paper, we perform comparative study of regularization approaches for variable selection under different correlation structures and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Extensive simulation studies and high-dimensional genomic data analysis on real datasets have demonstrated the advantage of the proposed rPGBS method over some of the most used regularization methods. In particular, rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to some recent methods addressing variable selection with strong correlations: Precision Lasso (Wang et al. in Bioinformatics 35:1181–1187, 2019) and Whitening Lasso (Zhu et al. in Bioinformatics 37:2238–2244, 2021). Moreover, rPGBS has been shown to be computationally efficient across various settings.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1139 - 1164"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135049935","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

On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data 关于渐进截尾广义X指数分布：（非）贝叶斯估计及其在癌症数据中的应用

Annals of Data Science Pub Date : 2023-06-15 DOI: 10.1007/s40745-023-00477-1

Kousik Maiti, Suchandan Kayal, Aditi Kar Gangopadhyay

引用次数: 0

A Survey on Differential Privacy for Medical Data Analysis 医疗数据分析中的差异隐私调查

Annals of Data Science Pub Date : 2023-06-10 DOI: 10.1007/s40745-023-00475-3

WeiKang Liu, Yanchun Zhang, Hong Yang, Qinxue Meng

引用次数: 0

Naïve Bayes Classifier Model for Detecting Spam Mails Naïve垃圾邮件检测的贝叶斯分类器模型

Annals of Data Science Pub Date : 2023-06-09 DOI: 10.1007/s40745-023-00479-z

Shrawan Kumar, Kavita Gupta, Manya Gupta

引用次数: 0

Artificial Intelligence Algorithms for Collaborative Book Recommender Systems 协同图书推荐系统的人工智能算法

Annals of Data Science Pub Date : 2023-06-08 DOI: 10.1007/s40745-023-00474-4

Clemens Tegetmeier, Arne Johannssen, Nataliya Chukhrova

引用次数: 0

On Poisson Moment Exponential Distribution with Associated Regression and INAR(1) Process 带关联回归和INAR(1)过程的泊松矩指数分布

Annals of Data Science Pub Date : 2023-06-08 DOI: 10.1007/s40745-023-00476-2

R. Maya, Jie Huang, M. R. Irshad, Fukang Zhu

{"title":"On Poisson Moment Exponential Distribution with Associated Regression and INAR(1) Process","authors":"R. Maya, Jie Huang, M. R. Irshad, Fukang Zhu","doi":"10.1007/s40745-023-00476-2","DOIUrl":"10.1007/s40745-023-00476-2","url":null,"abstract":"<div><p>Numerous studies have emphasised the significance of count data modeling and its applications to phenomena that occur in the real world. From this perspective, this article examines the traits and applications of the Poisson-moment exponential (PME) distribution in the contexts of time series analysis and regression analysis for real-world phenomena. The PME distribution is a novel one-parameter discrete distribution that can be used as a powerful alternative for the existing distributions for modeling over-dispersed count datasets. The advantages of the PME distribution, including the simplicity of the probability mass function and the explicit expressions of the functions of all the statistical properties, drove us to develop the inferential aspects and learn more about its practical applications. The unknown parameter is estimated using both maximum likelihood and moment estimation methods. Also, we present a parametric regression model based on the PME distribution for the count datasets. To strengthen the utility of the suggested distribution, we propose a new first-order integer-valued autoregressive (INAR(1)) process with PME innovations based on binomial thinning for modeling integer-valued time series with over-dispersion. Application to four real datasets confirms the empirical significance of the proposed model.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1741 - 1759"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264212","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

A New Compound Distribution and Its Applications in Over-dispersed Count Data 一种新的复合分布及其在过分散计数数据中的应用

Annals of Data Science Pub Date : 2023-06-07 DOI: 10.1007/s40745-023-00478-0

Peer Bilal Ahmad, Mohammad Kafeel Wani

{"title":"A New Compound Distribution and Its Applications in Over-dispersed Count Data","authors":"Peer Bilal Ahmad, Mohammad Kafeel Wani","doi":"10.1007/s40745-023-00478-0","DOIUrl":"10.1007/s40745-023-00478-0","url":null,"abstract":"<div><p>Every time variance exceeds mean, over-dispersed models are typically employed. This is the reason that over-dispersed models are such an important aspect of statistical modeling. In this work, the parameter of Poisson distribution is assumed to follow a new lifespan distribution called as Chris-Jerry distribution. The resulting compound distribution is an over-dispersed model known as the Poisson-Chris-Jerry distribution. As a result of deriving a general expression for the <i>r th</i> factorial moment, we acquired the moments about origin and the central moments. In addition to this, moment’s related measurements, generating functions, over-dispersion property, reliability characteristics, recurrence relation for probability, and other statistical qualities, have also been described. For the goal of estimating parameter of the suggested model, the maximum likelihood estimation and method of moment estimation have been addressed. The usefulness of maximum likelihood estimates has also been taken into consideration through a simulation study. We employed four real life data sets, examined the goodness-of-fit test, and considered additional standards such as the Akaike’s information criterion and Bayesian information criterion. The outcomes are compared with several potential models.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1799 - 1820"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46822534","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

Utilization of Priori Information in the Estimation of Population Mean for Time-Based Surveys 基于时间的调查中先验信息在人口均值估计中的应用

Annals of Data Science Pub Date : 2023-06-05 DOI: 10.1007/s40745-023-00472-6

Sanjay Kumar, Priyanka Chhaparwal

引用次数: 0

LADDERS: Log Based Anomaly Detection and Diagnosis for Enterprise Systems 梯子:基于日志的企业系统异常检测和诊断

Annals of Data Science Pub Date : 2023-06-04 DOI: 10.1007/s40745-023-00471-7

Sakib A. Mondal, Prashanth Rv, Sagar Rao, Arun Menon

{"title":"LADDERS: Log Based Anomaly Detection and Diagnosis for Enterprise Systems","authors":"Sakib A. Mondal, Prashanth Rv, Sagar Rao, Arun Menon","doi":"10.1007/s40745-023-00471-7","DOIUrl":"10.1007/s40745-023-00471-7","url":null,"abstract":"<div><p>Enterprise software can fail due to not only malfunction of application servers, but also due to performance degradation or non-availability of other servers or middle layers. Consequently, valuable time and resources are wasted in trying to identify the root cause of software failures. To address this, we have developed a framework called LADDERS. In LADDERS, anomalous incidents are detected from log events generated by various systems and KPIs (Key Performance Indicators) through an ensemble of supervised and unsupervised models. Without transaction identifiers, it is not possible to relate various events from different systems. LADDERS implements Recursive Parallel Causal Discovery (RPCD) to establish causal relationships among log events. The framework builds coresets using BICO to manage high volumes of log data during training and inferencing. An anomaly can cause a number of anomalies throughout the systems. LADDERS makes use of RPCD again to discover causal relationships among these anomalous events. Probable root causes are revealed from the causal graph and anomaly rating of events using a k-shortest path algorithm. We evaluated LADDERS using live logs from an enterprise system. The results demonstrate its effectiveness and efficiency for anomaly detection.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1165 - 1183"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46232475","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

Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model 使用修改后的 SIS 模型对累计感染病例进行跳跃式下降调整预测

Annals of Data Science Pub Date : 2023-05-15 DOI: 10.1007/s40745-023-00467-3

Rashi Mohta, Sravya Prathapani, Palash Ghosh

{"title":"Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model","authors":"Rashi Mohta, Sravya Prathapani, Palash Ghosh","doi":"10.1007/s40745-023-00467-3","DOIUrl":"10.1007/s40745-023-00467-3","url":null,"abstract":"<div><p>Accurate prediction of cumulative COVID-19 infected cases is essential for effectively managing the limited healthcare resources in India. Historically, epidemiological models have helped in controlling such epidemics. Models require accurate historical data to predict future outcomes. In our data, there were days exhibiting erratic, apparently anomalous jumps and drops in the number of daily reported COVID-19 infected cases that did not conform with the overall trend. Including those observations in the training data would most likely worsen model predictive accuracy. However, with existing epidemiological models it is not straightforward to determine, for a specific day, whether or not an outcome should be considered anomalous. In this work, we propose an algorithm to automatically identify anomalous ‘jump’ and ‘drop’ days, and then based upon the overall trend, the number of daily infected cases for those days is adjusted and the training data is amended using the adjusted observations. We applied the algorithm in conjunction with a recently proposed, modified Susceptible-Infected-Susceptible (SIS) model to demonstrate that prediction accuracy is improved after adjusting training data counts for apparent erratic anomalous jumps and drops.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"959 - 978"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135086225","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