Statistical Analysis and Data Mining最新文献_第8页

An algorithm to compare two-dimensional footwear outsole images using maximum cliques and speeded-up robust feature. 利用最大聚类和加速鲁棒特征比较二维鞋底图像的算法。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2020-04-01 Epub Date: 2020-02-21 DOI: 10.1002/sam.11449

Soyoung Park, Alicia Carriquiry

{"title":"An algorithm to compare two-dimensional footwear outsole images using maximum cliques and speeded-up robust feature.","authors":"Soyoung Park, Alicia Carriquiry","doi":"10.1002/sam.11449","DOIUrl":"10.1002/sam.11449","url":null,"abstract":"Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect's shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded-up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC-COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose-denoted MC-COMP-SURF-shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R-package shoeprintr.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"13 2","pages":"188-199"},"PeriodicalIF":1.3,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37774263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments. 适度计算环境下海量空间数据集的实用贝叶斯建模与推理。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2019-06-01 DOI: 10.1002/sam.11413

Lu Zhang, Abhirup Datta, Sudipto Banerjee

{"title":"Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments.","authors":"Lu Zhang, Abhirup Datta, Sudipto Banerjee","doi":"10.1002/sam.11413","DOIUrl":"https://doi.org/10.1002/sam.11413","url":null,"abstract":"With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has generated substantial interest over the last decade, already too vast to be summarized here, in scalable methodologies for analyzing large spatial datasets. Scalable spatial process models have been found especially attractive due to their richness and flexibility and, particularly so in the Bayesian paradigm, due to their presence in hierarchical model settings. However, the vast majority of research articles present in this domain have been geared toward innovative theory or more complex model development. Very limited attention has been accorded to approaches for easily implementable scalable hierarchical models for the practicing scientist or spatial analyst. This article devises massively scalable Bayesian approaches that can rapidly deliver inference on spatial process that are practically indistinguishable from inference obtained using more expensive alternatives. A key emphasis is on implementation within very standard (modest) computing environments (e.g., a standard desktop or laptop) using easily available statistical software packages. Key insights are offered regarding assumptions and approximations concerning practical efficiency.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"12 3","pages":"197-209"},"PeriodicalIF":1.3,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/sam.11413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10297504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs. 融合Lasso回归识别脑连接组图的差异相关性。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2018-10-01 Epub Date: 2018-07-11 DOI: 10.1002/sam.11382

Donghyeon Yu, Sang Han Lee, Johan Lim, Guanghua Xiao, R Cameron Craddock, Bharat B Biswal

引用次数: 3

Nonlinear variable selection with continuous outcome: a fully nonparametric incremental forward stagewise approach. 具有连续结果的非线性变量选择:一种完全非参数渐进式前向阶段方法。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2018-08-01 Epub Date: 2018-06-19 DOI: 10.1002/sam.11381

Tianwei Yu

引用次数: 0

The next-generation K-means algorithm. 下一代K-means算法。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2018-08-01 Epub Date: 2018-05-11 DOI: 10.1002/sam.11379

Eugene Demidenko

{"title":"The next-generation K-means algorithm.","authors":"Eugene Demidenko","doi":"10.1002/sam.11379","DOIUrl":"10.1002/sam.11379","url":null,"abstract":"Typically, when referring to a model-based classification, the mixture distribution approach is understood. In contrast, we revive the hard-classification model-based approach developed by Banfield and Raftery (1993) for which K-means is equivalent to the maximum likelihood (ML) estimation. The next-generation K-means algorithm does not end after the classification is achieved, but moves forward to answer the following fundamental questions: Are there clusters, how many clusters are there, what are the statistical properties of the estimated means and index sets, what is the distribution of the coefficients in the clusterwise regression, and how to classify multilevel data? The statistical model-based approach for the K-means algorithm is the key, because it allows statistical simulations and studying the properties of classification following the track of the classical statistics. This paper illustrates the application of the ML classification to testing the no-clusters hypothesis, to studying various methods for selection of the number of clusters using simulations, robust clustering using Laplace distribution, studying properties of the coefficients in clusterwise regression, and finally to multilevel data by marrying the variance components model with K-means.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"11 4","pages":"153-166"},"PeriodicalIF":1.3,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36368001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling. 基于混合建模的斑马鱼脑钙图像时间序列数据的全体积聚类。

IF 3.6 4区数学

Statistical Analysis and Data Mining Pub Date : 2018-02-01 Epub Date: 2017-12-06 DOI: 10.1002/sam.11366

Hien D Nguyen, Jeremy F P Ullmann, Geoffrey J McLachlan, Venkatakaushik Voleti, Wenze Li, Elizabeth M C Hillman, David C Reutens, Andrew L Janke

引用次数: 0

Random Forest Missing Data Algorithms. 随机森林缺失数据算法。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2017-12-01 Epub Date: 2017-06-13 DOI: 10.1002/sam.11348

Fei Tang, Hemant Ishwaran

引用次数: 377

Use and Communication of Probabilistic Forecasts. 概率预测的使用和交流。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2016-12-01 Epub Date: 2016-02-23 DOI: 10.1002/sam.11302

Adrian E Raftery

{"title":"Use and Communication of Probabilistic Forecasts.","authors":"Adrian E Raftery","doi":"10.1002/sam.11302","DOIUrl":"https://doi.org/10.1002/sam.11302","url":null,"abstract":"Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? I review experience with five problems where probabilistic forecasting played an important role. This leads me to identify five types of potential users: Low Stakes Users, who don't need probabilistic forecasts; General Assessors, who need an overall idea of the uncertainty in the forecast; Change Assessors, who need to know if a change is out of line with expectatations; Risk Avoiders, who wish to limit the risk of an adverse outcome; and Decision Theorists, who quantify their loss function and perform the decision-theoretic calculations. This suggests that it is important to interact with users and to consider their goals. The cognitive research tells us that calibration is important for trust in probability forecasts, and that it is important to match the verbal expression with the task. The cognitive load should be minimized, reducing the probabilistic forecast to a single percentile if appropriate. Probabilities of adverse events and percentiles of the predictive distribution of quantities of interest seem often to be the best way to summarize probabilistic forecasts. Formal decision theory has an important role, but in a limited range of applications.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"9 6","pages":"397-410"},"PeriodicalIF":1.3,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/sam.11302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34944896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 49

Hierarchical Models for Multiple, Rare Outcomes Using Massive Observational Healthcare Databases. 利用大规模观察性医疗保健数据库建立多种罕见结果的层次模型。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2016-08-01 Epub Date: 2016-07-17 DOI: 10.1002/sam.11324

Trevor R Shaddox, Patrick B Ryan, Martijn J Schuemie, David Madigan, Marc A Suchard

{"title":"Hierarchical Models for Multiple, Rare Outcomes Using Massive Observational Healthcare Databases.","authors":"Trevor R Shaddox, Patrick B Ryan, Martijn J Schuemie, David Madigan, Marc A Suchard","doi":"10.1002/sam.11324","DOIUrl":"10.1002/sam.11324","url":null,"abstract":"Clinical trials often lack power to identify rare adverse drug events (ADEs) and therefore cannot address the threat rare ADEs pose, motivating the need for new ADE detection techniques. Emerging national patient claims and electronic health record databases have inspired post-approval early detection methods like the Bayesian self-controlled case series (BSCCS) regression model. Existing BSCCS models do not account for multiple outcomes, where pathology may be shared across different ADEs. We integrate a pathology hierarchy into the BSCCS model by developing a novel informative hierarchical prior linking outcome-specific effects. Considering shared pathology drastically increases the dimensionality of the already massive models in this field. We develop an efficient method for coping with the dimensionality expansion by reducing the hierarchical model to a form amenable to existing tools. Through a synthetic study we demonstrate decreased bias in risk estimates for drugs when using conditions with different true risk and unequal prevalence. We also examine observational data from the MarketScan Lab Results dataset, exposing the bias that results from aggregating outcomes, as previously employed to estimate risk trends of warfarin and dabigatran for intracranial hemorrhage and gastrointestinal bleeding. We further investigate the limits of our approach by using extremely rare conditions. This research demonstrates that analyzing multiple outcomes simultaneously is feasible at scale and beneficial.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"9 4","pages":"260-268"},"PeriodicalIF":1.3,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423675/pdf/nihms799155.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34993872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear Joint Latent Variable Models and Integrative Tumor Subtype Discovery. 非线性联合潜在变量模型与综合肿瘤亚型发现。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2016-04-01 Epub Date: 2016-03-28 DOI: 10.1002/sam.11306

Binghui Liu, Xiaotong Shen, Wei Pan

{"title":"Nonlinear Joint Latent Variable Models and Integrative Tumor Subtype Discovery.","authors":"Binghui Liu, Xiaotong Shen, Wei Pan","doi":"10.1002/sam.11306","DOIUrl":"https://doi.org/10.1002/sam.11306","url":null,"abstract":"Integrative analysis has been used to identify clusters by integrating data of disparate types, such as deoxyribonucleic acid (DNA) copy number alterations and DNA methylation changes for discovering novel subtypes of tumors. Most existing integrative analysis methods are based on joint latent variable models, which are generally divided into two classes: joint factor analysis and joint mixture modeling, with continuous and discrete parameterizations of the latent variables respectively. Despite recent progresses, many issues remain. In particular, existing integration methods based on joint factor analysis may be inadequate to model multiple clusters due to the unimodality of the assumed Gaussian distribution, while those based on joint mixture modeling may not have the ability for dimension reduction and/or feature selection. In this paper, we employ a nonlinear joint latent variable model to allow for flexible modeling that can account for multiple clusters as well as conduct dimension reduction and feature selection. We propose a method, called integrative and regularized generative topographic mapping (irGTM), to perform simultaneous dimension reduction across multiple types of data while achieving feature selection separately for each data type. Simulations are performed to examine the operating characteristics of the methods, in which the proposed method compares favorably against the popular iCluster that is based on a linear joint latent variable model. Finally, a glioblastoma multiforme (GBM) dataset is examined.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"9 2","pages":"106-116"},"PeriodicalIF":1.3,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/sam.11306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35736330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1