Journal of data science : JDS最新文献_第2页

Identification of Optimal Combined Moderators for Time to Relapse 复吸时间最优组合调节因子的识别

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1107

Bang Wang, Yu Cheng, M. Levine

{"title":"Identification of Optimal Combined Moderators for Time to Relapse","authors":"Bang Wang, Yu Cheng, M. Levine","doi":"10.6339/23-jds1107","DOIUrl":"https://doi.org/10.6339/23-jds1107","url":null,"abstract":"Identifying treatment effect modifiers (i.e., moderators) plays an essential role in improving treatment efficacy when substantial treatment heterogeneity exists. However, studies are often underpowered for detecting treatment effect modifiers, and exploratory analyses that examine one moderator per statistical model often yield spurious interactions. Therefore, in this work, we focus on creating an intuitive and readily implementable framework to facilitate the discovery of treatment effect modifiers and to make treatment recommendations for time-to-event outcomes. To minimize the impact of a misspecified main effect and avoid complex modeling, we construct the framework by matching the treated with the controls and modeling the conditional average treatment effect via regressing the difference in the observed outcomes of a matched pair on the averaged moderators. Inverse-probability-of-censoring weighting is used to handle censored observations. As matching is the foundation of the proposed methods, we explore different matching metrics and recommend the use of Mahalanobis distance when both continuous and categorical moderators are present. After matching, the proposed framework can be flexibly combined with popular variable selection and prediction methods such as linear regression, least absolute shrinkage and selection operator (Lasso), and random forest to create different combinations of potential moderators. The optimal combination is determined by the out-of-bag prediction error and the area under the receiver operating characteristic curve in making correct treatment recommendations. We compare the performance of various combined moderators through extensive simulations and the analysis of real trial data. Our approach can be easily implemented using existing R packages, resulting in a straightforward optimal combined moderator to make treatment recommendations.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320573","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

Revisiting the Use of Generalized Least Squares in Time Series Regression Models 回顾广义最小二乘在时间序列回归模型中的应用

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1108

Yue Fang, S. Koreisha, Q. Shao

引用次数: 0

Analyzing the Rainfall Pattern in Honduras Through Non-Homogeneous Hidden Markov Models 用非齐次隐马尔可夫模型分析洪都拉斯降雨模式

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1091

Gustavo Alexis Sabillón, D. Zuanetti

引用次数: 0

Network A/B Testing: Nonparametric Statistical Significance Test Based on Cluster-Level Permutation 网络A/B测试:基于集群水平排列的非参数统计显著性检验

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1112

Hongwei Shang, Xiaolin Shi, Bai Jiang

{"title":"Network A/B Testing: Nonparametric Statistical Significance Test Based on Cluster-Level Permutation","authors":"Hongwei Shang, Xiaolin Shi, Bai Jiang","doi":"10.6339/23-jds1112","DOIUrl":"https://doi.org/10.6339/23-jds1112","url":null,"abstract":"A/B testing is widely used for comparing two versions of a product and evaluating new proposed product features. It is of great importance for decision-making and has been applied as a golden standard in the IT industry. It is essentially a form of two-sample statistical hypothesis testing. Average treatment effect (ATE) and the corresponding p-value can be obtained under certain assumptions. One key assumption in traditional A/B testing is the stable-unit-treatment-value assumption (SUTVA): there is no interference among different units. It means that the observation on one unit is unaffected by the particular assignment of treatments to the other units. Nonetheless, interference is very common in social network settings where people communicate and spread information to their neighbors. Therefore, the SUTVA assumption is violated. Analysis ignoring this network effect will lead to biased estimation of ATE. Most existing works focus mainly on the design of experiment and data analysis in order to produce estimators with good performance in regards to bias and variance. Little attention has been paid to the calculation of p-value. We work on the calculation of p-value for the ATE estimator in network A/B tests. After a brief review of existing research methods on design of experiment based on graph cluster randomization and different ATE estimation methods, we propose a permutation method for calculating p-value based on permutation test at the cluster level. The effectiveness of the method against that based on individual-level permutation is validated in a simulation study mimicking realistic settings.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71321036","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: 1

Editorial: Advances in Network Data Science 社论:网络数据科学进展

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds213edi

Yuguo Chen, Daniel Sewell, Panpan Zhang, Xuening Zhu

{"title":"Editorial: Advances in Network Data Science","authors":"Yuguo Chen, Daniel Sewell, Panpan Zhang, Xuening Zhu","doi":"10.6339/23-jds213edi","DOIUrl":"https://doi.org/10.6339/23-jds213edi","url":null,"abstract":"This special issue features nine articles on “Advances in Network Data Science”. Data science is an interdisciplinary research ﬁeld utilizing scientiﬁc methods to facilitate knowledge and insights from structured and unstructured data across a broad range of domains. Network data are proliferating in many ﬁelds, and network data analysis has become a burgeoning research in the data science community. Due to the nature of heterogeneity and complexity of network data, classical statistical approaches for network model ﬁtting face a great deal of challenges, especially for large-scale network data. Therefore, it becomes crucial to develop advanced methodological and computational tools to cope with challenges associated with massive and complex network data analyses. This special issue highlights some recent studies in the area of network data analysis, showcasing a variety of contributions in statistical methodology, two real-world applications, a software package for network generation, and a survey on handling missing values in networks. Five articles are published in the Statistical Data Science Section. Wang and Resnick (2023) employed point processes to investigate the macroscopic growth dynamics of geographically concentrated regional networks. They discovered that during the startup phase, a self-exciting point process eﬀectively modeled the growth process, and subsequently, the growth of links could be suitably described by a non-homogeneous Poisson process. Komolafe","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71321135","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

FROSTY: A High-Dimensional Scale-Free Bayesian Network Learning Method FROSTY:一种高维无标度贝叶斯网络学习方法

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1097

Joshua Bang, Sang-Yun Oh

引用次数: 2

Assessment of Projection Pursuit Index for Classifying High Dimension Low Sample Size Data in R R中高维低样本量数据分类的投影寻踪指标评价

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1096

Zhaoxing Wu, Chunming Zhang

{"title":"Assessment of Projection Pursuit Index for Classifying High Dimension Low Sample Size Data in R","authors":"Zhaoxing Wu, Chunming Zhang","doi":"10.6339/23-jds1096","DOIUrl":"https://doi.org/10.6339/23-jds1096","url":null,"abstract":"Analyzing “large p small n” data is becoming increasingly paramount in a wide range of application fields. As a projection pursuit index, the Penalized Discriminant Analysis ($mathrm{PDA}$) index, built upon the Linear Discriminant Analysis ($mathrm{LDA}$) index, is devised in Lee and Cook (2010) to classify high-dimensional data with promising results. Yet, there is little information available about its performance compared with the popular Support Vector Machine ($mathrm{SVM}$). This paper conducts extensive numerical studies to compare the performance of the $mathrm{PDA}$ index with the $mathrm{LDA}$ index and $mathrm{SVM}$, demonstrating that the $mathrm{PDA}$ index is robust to outliers and able to handle high-dimensional datasets with extremely small sample sizes, few important variables, and multiple classes. Analyses of several motivating real-world datasets reveal the practical advantages and limitations of individual methods, suggesting that the $mathrm{PDA}$ index provides a useful alternative tool for classifying complex high-dimensional data. These new insights, along with the hands-on implementation of the $mathrm{PDA}$ index functions in the R package classPP, facilitate statisticians and data scientists to make effective use of both sets of classification tools.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320830","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: 2

Binary Classification of Malignant Mesothelioma: A Comparative Study 恶性间皮瘤二元分类的比较研究

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1090

Ted Si Yuan Cheng, Xiyue Liao

引用次数: 3

Building a Foundation for More Flexible A/B Testing: Applications of Interim Monitoring to Large Scale Data 为更灵活的a /B测试奠定基础:中期监控在大规模数据中的应用

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1099

Wenru Zhou, Miranda Kroehl, Maxene Meier, A. Kaizer

{"title":"Building a Foundation for More Flexible A/B Testing: Applications of Interim Monitoring to Large Scale Data","authors":"Wenru Zhou, Miranda Kroehl, Maxene Meier, A. Kaizer","doi":"10.6339/23-jds1099","DOIUrl":"https://doi.org/10.6339/23-jds1099","url":null,"abstract":"The use of error spending functions and stopping rules has become a powerful tool for conducting interim analyses. The implementation of an interim analysis is broadly desired not only in traditional clinical trials but also in A/B tests. Although many papers have summarized error spending approaches, limited work has been done in the context of large-scale data that assists in finding the “optimal” boundary. In this paper, we summarized fifteen boundaries that consist of five error spending functions that allow early termination for futility, difference, or both, as well as a fixed sample size design without interim monitoring. The simulation is based on a practical A/B testing problem comparing two independent proportions. We examine sample sizes across a range of values from 500 to 250,000 per arm to reflect different settings where A/B testing may be utilized. The choices of optimal boundaries are summarized using a proposed loss function that incorporates different weights for the expected sample size under a null experiment with no difference between variants, the expected sample size under an experiment with a difference in the variants, and the maximum sample size needed if the A/B test did not stop early at an interim analysis. The results are presented for simulation settings based on adequately powered, under-powered, and over-powered designs with recommendations for selecting the “optimal” design in each setting.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320881","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: 2

An Assessment of Crop-Specific Land Cover Predictions Using High-Order Markov Chains and Deep Neural Networks 基于高阶马尔可夫链和深度神经网络的作物特定土地覆盖预测评估

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1098

L. Sartore, C. Boryan, Andrew Dau, P. Willis

{"title":"An Assessment of Crop-Specific Land Cover Predictions Using High-Order Markov Chains and Deep Neural Networks","authors":"L. Sartore, C. Boryan, Andrew Dau, P. Willis","doi":"10.6339/23-jds1098","DOIUrl":"https://doi.org/10.6339/23-jds1098","url":null,"abstract":"High-Order Markov Chains (HOMC) are conventional models, based on transition probabilities, that are used by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) to study crop-rotation patterns over time. However, HOMCs routinely suffer from sparsity and identifiability issues because the categorical data are represented as indicator (or dummy) variables. In fact, the dimension of the parametric space increases exponentially with the order of HOMCs required for analysis. While parsimonious representations reduce the number of parameters, as has been shown in the literature, they often result in less accurate predictions. Most parsimonious models are trained on big data structures, which can be compressed and efficiently processed using alternative algorithms. Consequently, a thorough evaluation and comparison of the prediction results obtain using a new HOMC algorithm and different types of Deep Neural Networks (DNN) across a range of agricultural conditions is warranted to determine which model is most appropriate for operational crop specific land cover prediction of United States (US) agriculture. In this paper, six neural network models are applied to crop rotation data between 2011 and 2021 from six agriculturally intensive counties, which reflect the range of major crops grown and a variety of crop rotation patterns in the Midwest and southern US. The six counties include: Renville, North Dakota; Perkins, Nebraska; Hale, Texas; Livingston, Illinois; McLean, Illinois; and Shelby, Ohio. Results show the DNN models achieve higher overall prediction accuracy for all counties in 2021. The proposed DNN models allow for the ingestion of long time series data, and robustly achieve higher accuracy values than a new HOMC algorithm considered for predicting crop specific land cover in the US.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71321012","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: 2