Statistical Analysis and Data Mining: The ASA Data Science Journal最新文献_第5页

Traditional kriging versus modern Gaussian processes for large‐scale mining data 大规模挖掘数据的传统克里格和现代高斯过程

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-07-20 DOI: 10.1002/sam.11635

R. Christianson, R. Pollyea, R. Gramacy

{"title":"Traditional kriging versus modern Gaussian processes for large‐scale mining data","authors":"R. Christianson, R. Pollyea, R. Gramacy","doi":"10.1002/sam.11635","DOIUrl":"https://doi.org/10.1002/sam.11635","url":null,"abstract":"The canonical technique for nonlinear modeling of spatial/point‐referenced data is known as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling and statistical learning. This article reviews many similarities shared between kriging and GPs, but also highlights some important differences. One is that GPs impose a process that can be used to automate kernel/variogram inference, thus removing the human from the loop. The GP framework also suggests a probabilistically valid means of scaling to handle a large corpus of training data, that is, an alternative to ordinary kriging. Finally, recent GP implementations are tailored to make the most of modern computing architectures, such as multi‐core workstations and multi‐node supercomputers. We argue that such distinctions are important even in classically geostatistical settings. To back that up, we present out‐of‐sample validation exercises using two, real, large‐scale borehole data sets acquired in the mining of gold and other minerals. We compare classic kriging with several variations of modern GPs and conclude that the latter is more economical (fewer human and compute resources), more accurate and offers better uncertainty quantification. We go on to show how the fully generative modeling apparatus provided by GPs can gracefully accommodate left‐censoring of small measurements, as commonly occurs in mining data and other borehole assays.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115616927","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

Online embedding and clustering of evolving data streams 动态数据流的在线嵌入和聚类

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-07-06 DOI: 10.1002/sam.11590

Alaettin Zubaroğlu, V. Atalay

{"title":"Online embedding and clustering of evolving data streams","authors":"Alaettin Zubaroğlu, V. Atalay","doi":"10.1002/sam.11590","DOIUrl":"https://doi.org/10.1002/sam.11590","url":null,"abstract":"Number of connected devices is steadily increasing and this trend is expected to continue in the near future. Connected devices continuously generate data streams and the data streams may often be high dimensional and contain concept drift. Clustering is one of the most suitable methods for real‐time data stream processing, since clustering can be applied with less prior information about the data. Also, data embedding makes the visualization of high dimensional data possible and may simplify clustering process. There exist several data stream clustering algorithms in the literature; however, no data stream embedding method exists. Uniform Manifold Approximation and Projection (UMAP) is a data embedding algorithm that is suitable to be applied on stationary (stable) data streams, though it cannot adapt concept drift. In this study, we describe a novel method EmCStream, to apply UMAP on evolving (nonstationary) data streams, to detect and adapt concept drift and to cluster embedded data instances using a distance or partitioning‐based clustering algorithm. We have evaluated EmCStream against the state‐of‐the‐art stream clustering algorithms using both synthetic and real data streams containing concept drift. EmCStream outperforms DenStream and CluStream, in terms of clustering quality, on both synthetic and real evolving data streams. Datasets and code of this study are available online at https://gitlab.com/alaettinzubaroglu/emcstream.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030167","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

Kernel learning with nonconvex ramp loss 具有非凸斜坡损失的核学习

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-06-08 DOI: 10.1002/sam.11588

Xijun Liang, Zhipeng Zhang, Xingke Chen, Ling Jian

引用次数: 1

A linear time method for the detection of collective and point anomalies 一种用于集体和点异常检测的线性时间方法

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-06-03 DOI: 10.1002/sam.11586

Alexander T. M. Fisch, I. Eckley, P. Fearnhead

引用次数: 11

Some Bayesian biclustering methods: Modeling and inference 一些贝叶斯双聚类方法:建模和推理

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-04-20 DOI: 10.1002/sam.11584

A. Chakraborty, S. Vardeman

引用次数: 0

Biclustering high‐frequency financial time series based on information theory 基于信息论的高频金融时间序列双聚类

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-04-01 DOI: 10.1002/sam.11581

Haitao Liu, J. Zou, N. Ravishanker

{"title":"Biclustering high‐frequency financial time series based on information theory","authors":"Haitao Liu, J. Zou, N. Ravishanker","doi":"10.1002/sam.11581","DOIUrl":"https://doi.org/10.1002/sam.11581","url":null,"abstract":"Clustering a large number of time series into relatively homogeneous groups is a well‐studied unsupervised learning technique that has been widely used for grouping financial instruments (say, stocks) based on their stochastic properties across the entire time period under consideration. However, clustering algorithms ignore the notion of biclustering, that is, grouping of stocks only within a subset of times rather than over the entire time period. Biclustering algorithms enable grouping of stocks and times simultaneously, and thus facilitate improved pattern extraction for informed trading strategies. While biclustering methods may be employed for grouping low‐frequency (daily) financial data, their use with high‐frequency financial time series of intra‐day trading data is especially useful. This paper develops a biclustering algorithm based on pairwise or groupwise mutual information between one‐minute averaged stock returns within a trading day, using jackknife estimation of mutual information (JMI). We construct a multiple day time series biclustering (MI‐MDTSB) algorithm that can capture refined and local comovement patterns between groups of stocks over a subset of continuous time points. Extensive numerical studies based on high‐frequency returns data reveal interesting intra‐day patterns among different asset groups and sectors.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116306113","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

Adversarially robust subspace learning in the spiked covariance model 尖峰协方差模型的对抗性鲁棒子空间学习

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-03-18 DOI: 10.1002/sam.11580

Fei Sha, Ruizhi Zhang

引用次数: 0

A tree‐based gene–environment interaction analysis with rare features 基于树的罕见特征基因-环境相互作用分析

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-03-01 DOI: 10.1002/sam.11578

Mengque Liu, Qingzhao Zhang, Shuangge Ma

{"title":"A tree‐based gene–environment interaction analysis with rare features","authors":"Mengque Liu, Qingzhao Zhang, Shuangge Ma","doi":"10.1002/sam.11578","DOIUrl":"https://doi.org/10.1002/sam.11578","url":null,"abstract":"Gene–environment (G‐E) interaction analysis plays a critical role in understanding and modeling complex diseases. Compared to main‐effect‐only analysis, it is more seriously challenged by higher dimensionality, weaker signals, and the unique “main effects, interactions” variable selection hierarchy. In joint G‐E interaction analysis under which a large number of G factors are analyzed in a single model, effort tailored to rare features (e.g., SNPs with low minor allele frequencies) has been limited. Existing investigations on rare features have been mostly focused on marginal analysis, where various data aggregation techniques have been developed, and hypothesis testings have been conducted to identify significant aggregated features. However, such techniques cannot be extended to joint G‐E interaction analysis. In this study, building on a very recent tree‐based data aggregation technique, which has been developed for main‐effect‐only analysis, we develop a new G‐E interaction analysis approach tailored to rare features. The adopted data aggregation technique allows for more efficient information borrowing from neighboring rare features. Similar to some existing state‐of‐the‐art ones, the proposed approach adopts penalization for variable selection, regularized estimation, and respect of the variable selection hierarchy. Simulation shows that it has more accurate identification of important interactions and main effects than several competing alternatives. In the analysis of NFBC1966 study, the proposed approach leads to findings different from the alternatives and with satisfactory prediction and stability performance.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"50 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121008731","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

A modified least angle regression algorithm for interaction selection with heredity 遗传互作选择的改进最小角回归算法

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-02-28 DOI: 10.1002/sam.11577

Woosung Kim, Seonghyeon Kim, M. Na, Yongdai Kim

{"title":"A modified least angle regression algorithm for interaction selection with heredity","authors":"Woosung Kim, Seonghyeon Kim, M. Na, Yongdai Kim","doi":"10.1002/sam.11577","DOIUrl":"https://doi.org/10.1002/sam.11577","url":null,"abstract":"In many practical problems, the main effects alone may not be enough to capture the relationship between the response and predictors, and the interaction effects are often of interest to scientific researchers. In considering a regression model with main effects and all possible two‐way interaction effects, which we call the two‐way interaction model, there is an important challenge—computational burden. One way to reduce the aforementioned problems is to consider the heredity constraint between the main and interaction effects. The heredity constraint assumes that a given interaction effect is significant only when the corresponding main effects are significant. Various sparse penalized methods to reflect the heredity constraint have been proposed, but those algorithms are still computationally demanding and can be applied to data where the dimension of the main effects is only few hundreds. In this paper, we propose a modification of the LARS algorithm for selecting interaction effects under the heredity constraint, which can be applied to high‐dimensional data. Our numerical studies confirm that the proposed modified LARS algorithm is much faster and spends less memory than its competitors but has comparable prediction accuracies when the dimension of covariates is large.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130698684","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

Local influence analysis for the sliced average third‐moment estimation 切片平均三阶矩估计的局部影响分析

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2022-02-23 DOI: 10.1002/sam.11575

Weidong Rao, Xiaofei Liu, Fei Chen

引用次数: 0