Journal of data science : JDS最新文献_第3页

Impact of Bias Correction of the Least Squares Estimation on Bootstrap Confidence Intervals for Bifurcating Autoregressive Models 最小二乘估计偏差校正对分岔自回归模型自举置信区间的影响

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1092

T. Elbayoumi, S. Mostafa

引用次数: 0

Computing Pseudolikelihood Estimators for Exponential-Family Random Graph Models 计算指数族随机图模型的伪似然估计量

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1094

Christian S. Schmid, David R. Hunter

引用次数: 3

Covid-19 Vaccine Efficacy: Accuracy Assessment, Comparison, and Caveats Covid-19疫苗有效性:准确性评估、比较和注意事项

Journal of data science : JDS Pub Date : 2023-01-01 DOI: 10.6339/23-jds1089

Wenjiang J. Fu, Jieni Li, P. Scheet

引用次数: 0

The Second Competition on Spatial Statistics for Large Datasets 第二届大型数据集空间统计竞赛

Journal of data science : JDS Pub Date : 2022-11-06 DOI: 10.6339/22-jds1076

Sameh Abdulah, Faten S. Alamri, Pratik Nag, Ying Sun, H. Ltaief, D. Keyes, M. Genton

{"title":"The Second Competition on Spatial Statistics for Large Datasets","authors":"Sameh Abdulah, Faten S. Alamri, Pratik Nag, Ying Sun, H. Ltaief, D. Keyes, M. Genton","doi":"10.6339/22-jds1076","DOIUrl":"https://doi.org/10.6339/22-jds1076","url":null,"abstract":"In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our ExaGeoStat software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42045278","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: 5

Vecchia Approximations and Optimization for Multivariate Matérn Models 多元数学模型的Vecchia逼近与优化

Journal of data science : JDS Pub Date : 2022-10-17 DOI: 10.6339/22-jds1074

Youssef A. Fahmy, J. Guinness

引用次数: 2

Geostatistics for Large Datasets on Riemannian Manifolds: A Matrix-Free Approach 黎曼流形上大数据集的地质统计学:无矩阵方法

Journal of data science : JDS Pub Date : 2022-08-26 DOI: 10.6339/22-jds1075

M. Pereira, N. Desassis, D. Allard

引用次数: 6

EVIboost for the Estimation of Extreme Value Index Under Heterogeneous Extremes 非均匀极值下极值指数估计的EVIboost

Journal of data science : JDS Pub Date : 2022-05-28 DOI: 10.6339/22-jds1067

Jiaxi Wang, Yanxi Hou, Xingchi Li, Tiandong Wang

引用次数: 0

Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability Estimation 鲁棒可扩展的非参数多类概率估计的线性算法

Journal of data science : JDS Pub Date : 2022-05-25 DOI: 10.6339/22-jds1069

Liyun Zeng, Hao Helen Zhang

{"title":"Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability Estimation","authors":"Liyun Zeng, Hao Helen Zhang","doi":"10.6339/22-jds1069","DOIUrl":"https://doi.org/10.6339/22-jds1069","url":null,"abstract":"Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for K-class problems (Wu et al., 2010; Wang et al., 2019), where K is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in K. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in K. Though not the most efficient in computation, the OVA is found to have the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate their finite sample performance.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44600781","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

Incorporating Interventions to an Extended SEIRD Model with Vaccination: Application to COVID-19 in Qatar 将干预措施纳入扩展SEIRD模型与疫苗接种:在卡塔尔COVID-19中的应用

Journal of data science : JDS Pub Date : 2022-04-23 DOI: 10.6339/23-JDS1105

Elizabeth B Amona, R. Ghanam, E. Boone, Indranil Sahoo, L. Abu-Raddad

引用次数: 1

Causal Discovery for Observational Sciences Using Supervised Machine Learning 使用监督机器学习的观察科学因果发现

Journal of data science : JDS Pub Date : 2022-02-25 DOI: 10.6339/23-jds1088

A. H. Petersen, J. Ramsey, C. Ekstrøm, P. Spirtes

{"title":"Causal Discovery for Observational Sciences Using Supervised Machine Learning","authors":"A. H. Petersen, J. Ramsey, C. Ekstrøm, P. Spirtes","doi":"10.6339/23-jds1088","DOIUrl":"https://doi.org/10.6339/23-jds1088","url":null,"abstract":"Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorithms are empirical methods for constructing such causal models from data. Several asymptotically correct discovery methods already exist, but they generally struggle on smaller samples. Moreover, most methods focus on very sparse causal models, which may not always be a realistic representation of real-life data generating mechanisms. Finally, while causal relationships suggested by the methods often hold true, their claims about causal non-relatedness have high error rates. This non-conservative error trade off is not ideal for observational sciences, where the resulting model is directly used to inform causal inference: A causal model with many missing causal relations entails too strong assumptions and may lead to biased effect estimates. We propose a new causal discovery method that addresses these three shortcomings: Supervised learning discovery (SLdisco). SLdisco uses supervised machine learning to obtain a mapping from observational data to equivalence classes of causal models. We evaluate SLdisco in a large simulation study based on Gaussian data and we consider several choices of model size and sample size. We find that SLdisco is more conservative, only moderately less informative and less sensitive towards sample size than existing procedures. We furthermore provide a real epidemiological data application. We use random subsampling to investigate real data performance on small samples and again find that SLdisco is less sensitive towards sample size and hence seems to better utilize the information available in small datasets.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45032104","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: 7