Statistical Analysis and Data Mining最新文献_第6页

Driving mode analysis—How uncertain functional inputs propagate to an output 驱动模式分析-不确定的功能输入如何传播到输出

4区数学

Statistical Analysis and Data Mining Pub Date : 2023-10-06 DOI: 10.1002/sam.11646

Scott A. Vander Wiel, Michael J. Grosskopf, Isaac J. Michaud, Denise Neudecker

{"title":"Driving mode analysis—How uncertain functional inputs propagate to an output","authors":"Scott A. Vander Wiel, Michael J. Grosskopf, Isaac J. Michaud, Denise Neudecker","doi":"10.1002/sam.11646","DOIUrl":"https://doi.org/10.1002/sam.11646","url":null,"abstract":"Abstract Driving mode analysis elucidates how correlated features of uncertain functional inputs jointly propagate to produce uncertainty in the output of a computation. Uncertain input functions are decomposed into three terms: the mean functions, a zero‐mean driving mode, and zero‐mean residual. The random driving mode varies along a single direction, having fixed functional shape and random scale. It is uncorrelated with the residual, and under linear error propagation, it produces an output variance equal to that of the full input uncertainty. Finally, the driving mode best represents how input uncertainties propagate to the output because it minimizes expected squared Mahalanobis distance amongst competitors. These characteristics recommend interpretation of the driving mode as the single‐degree‐of‐freedom component of input uncertainty that drives output uncertainty. We derive the functional driving mode, show its superiority to other seemingly sensible definitions, and demonstrate the utility of driving mode analysis in an application. The application is the simulation of neutron transport in criticality experiments. The uncertain input functions are nuclear data that describe how Pu reacts to bombardment by neutrons. Visualization of the driving mode helps scientists understand what aspects of correlated functional uncertainty have effects that either reinforce or cancel one another in propagating to the output of the simulation.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134944202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Residuals and diagnostics for multinomial regression models 多项回归模型的残差和诊断

4区数学

Statistical Analysis and Data Mining Pub Date : 2023-09-29 DOI: 10.1002/sam.11645

Eric A. E. Gerber, Bruce A. Craig

引用次数: 0

Stratified learning: A general‐purpose statistical method for improved learning under covariate shift 分层学习:一种在协变量移位下改善学习的通用统计方法

4区数学

Statistical Analysis and Data Mining Pub Date : 2023-09-29 DOI: 10.1002/sam.11643

Maximilian Autenrieth, David A. Van Dyk, Roberto Trotta, David C. Stenning

引用次数: 1

On difference‐based gradient estimation in nonparametric regression 非参数回归中基于差分的梯度估计

4区数学

Statistical Analysis and Data Mining Pub Date : 2023-09-16 DOI: 10.1002/sam.11644

Maoyu Zhang, Wenlin Dai

引用次数: 0

Integrative Learning of Structured High-Dimensional Data from Multiple Datasets. 从多个数据集整合学习结构化高维数据

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-04-01 Epub Date: 2022-11-08 DOI: 10.1002/sam.11601

Changgee Chang, Zongyu Dai, Jihwan Oh, Qi Long

{"title":"Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.","authors":"Changgee Chang, Zongyu Dai, Jihwan Oh, Qi Long","doi":"10.1002/sam.11601","DOIUrl":"10.1002/sam.11601","url":null,"abstract":"Integrative learning of multiple datasets has the potential to mitigate the challenge of small n and large p that is often encountered in analysis of big biomedical data such as genomics data. Detection of weak yet important signals can be enhanced by jointly selecting features for all datasets. However, the set of important features may not always be the same across all datasets. Although some existing integrative learning methods allow heterogeneous sparsity structure where a subset of datasets can have zero coefficients for some selected features, they tend to yield reduced efficiency, reinstating the problem of losing weak important signals. We propose a new integrative learning approach which can not only aggregate important signals well in homogeneous sparsity structure, but also substantially alleviate the problem of losing weak important signals in heterogeneous sparsity structure. Our approach exploits a priori known graphical structure of features and encourages joint selection of features that are connected in the graph. Integrating such prior information over multiple datasets enhances the power, while also accounting for the heterogeneity across datasets. Theoretical properties of the proposed method are investigated. We also demonstrate the limitations of existing approaches and the superiority of our method using a simulation study and analysis of gene expression data from ADNI.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9511811","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

A Tutorial on Generative Adversarial Networks with Application to Classification of Imbalanced Data. 生成对抗性网络教程及其在不平衡数据分类中的应用。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2022-10-01 Epub Date: 2021-12-31 DOI: 10.1002/sam.11570

Yuxiao Huang, Kara G Fields, Yan Ma

{"title":"A Tutorial on Generative Adversarial Networks with Application to Classification of Imbalanced Data.","authors":"Yuxiao Huang, Kara G Fields, Yan Ma","doi":"10.1002/sam.11570","DOIUrl":"10.1002/sam.11570","url":null,"abstract":"A challenge unique to classification model development is imbalanced data. In a binary classification problem, class imbalance occurs when one class, the minority group, contains significantly fewer samples than the other class, the majority group. In imbalanced data, the minority class is often the class of interest (e.g., patients with disease). However, when training a classifier on imbalanced data, the model will exhibit bias towards the majority class and, in extreme cases, may ignore the minority class completely. A common strategy for addressing class imbalance is data augmentation. However, traditional data augmentation methods are associated with overfitting, where the model is fit to the noise in the data. In this tutorial we introduce an advanced method for data augmentation: Generative Adversarial Networks (GANs). The advantages of GANs over traditional data augmentation methods are illustrated using the Breast Cancer Wisconsin study. To promote the adoption of GANs for data augmentation, we present an end-to-end pipeline that encompasses the complete life cycle of a machine learning project along with alternatives and good practices both in the paper and in a separate video. Our code, data, full results and video tutorial are publicly available in the paper's github repository.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9529000/pdf/nihms-1766432.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33488851","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: 3

Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models. 基于回归的非超自然图形模型贝叶斯估计与结构学习。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2022-10-01 Epub Date: 2022-02-28 DOI: 10.1002/sam.11576

Jami J Mulgrave, Subhashis Ghosal

{"title":"Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models.","authors":"Jami J Mulgrave, Subhashis Ghosal","doi":"10.1002/sam.11576","DOIUrl":"https://doi.org/10.1002/sam.11576","url":null,"abstract":"A nonparanormal graphical model is a semiparametric generalization of a Gaussian graphical model for continuous variables in which it is assumed that the variables follow a Gaussian graphical model only after some unknown smooth monotone transformations. We consider a Bayesian approach to inference in a nonparanormal graphical model in which we put priors on the unknown transformations through a random series based on B-splines. We use a regression formulation to construct the likelihood through the Cholesky decomposition on the underlying precision matrix of the transformed variables and put shrinkage priors on the regression coefficients. We apply a plug-in variational Bayesian algorithm for learning the sparse precision matrix and compare the performance to a posterior Gibbs sampling scheme in a simulation study. We finally apply the proposed methods to a microarray data set. The proposed methods have better performance as the dimension increases, and in particular, the variational Bayesian approach has the potential to speed up the estimation in the Bayesian nonparanormal graphical model without the Gaussianity assumption while retaining the information to construct the graph.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33460008","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

Evaluation and interpretation of driving risks: Automobile claim frequency modeling with telematics data 驾驶风险的评估与解释:基于远程信息处理数据的汽车索赔频率建模

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2022-09-28 DOI: 10.1002/sam.11599

Yaqian Gao, Yifan Huang, Shengwang Meng

{"title":"Evaluation and interpretation of driving risks: Automobile claim frequency modeling with telematics data","authors":"Yaqian Gao, Yifan Huang, Shengwang Meng","doi":"10.1002/sam.11599","DOIUrl":"https://doi.org/10.1002/sam.11599","url":null,"abstract":"With the development of vehicle telematics and data mining technology, usage-based insurance (UBI) has aroused widespread interest from both academia and industry. The extensive driving behavior features make it possible to further understand the risks of insured vehicles, but pose challenges in the identification and interpretation of important ratemaking factors. This study, based on the telematics data of policyholders in China's mainland, analyzes insurance claim frequency of commercial trucks using both Poisson regression and several machine learning models, including regression tree, random forest, gradient boosting tree, XGBoost and neural network. After selecting the best model, we analyze feature importance, feature effects and the contribution of each feature to the prediction from an actuarial perspective. Our empirical study shows that XGBoost greatly outperforms the traditional models and detects some important risk factors, such as the average speed, the average mileage traveled per day, the fraction of night driving, the number of sudden brakes and the fraction of left/right turns at intersections. These features usually have a nonlinear effect on driving risk, and there are complex interactions between features. To further distinguish high−/low-risk drivers, we run supervised clustering for risk segmentation according to drivers' driving habits. In summary, this study not only provide a more accurate prediction of driving risk, but also greatly satisfy the interpretability requirements of insurance regulators and risk management.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A General Iterative Clustering Algorithm. 一种通用迭代聚类算法。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2022-08-01 Epub Date: 2022-01-31 DOI: 10.1002/sam.11573

Ziqiang Lin, Eugene Laska, Carole Siegel

{"title":"A General Iterative Clustering Algorithm.","authors":"Ziqiang Lin, Eugene Laska, Carole Siegel","doi":"10.1002/sam.11573","DOIUrl":"https://doi.org/10.1002/sam.11573","url":null,"abstract":"The quality of a cluster analysis of unlabeled units depends on the quality of the between units dissimilarity measures. Data dependent dissimilarity is more objective than data independent geometric measures such as Euclidean distance. As suggested by Breiman, many data driven approaches are based on decision tree ensembles, such as a random forest (RF), that produce a proximity matrix that can easily be transformed into a dissimilarity matrix. A RF can be obtained using labels that distinguish units with real data from units with synthetic data. The resulting dissimilarity matrix is input to a clustering program and units are assigned labels corresponding to cluster membership. We introduce a General Iterative Cluster (GIC) algorithm that improves the proximity matrix and clusters of the base RF. The cluster labels are used to grow a new RF yielding an updated proximity matrix which is entered into the clustering program. The process is repeated until convergence. The same procedure can be used with many base procedures such as the Extremely Randomized Tree ensemble. We evaluate the performance of the GIC algorithm using benchmark and simulated data sets. The properties measured by the Silhouette Score are substantially superior to the base clustering algorithm. The GIC package has been released in R: https://cran.r-project.org/web/packages/GIC/index.html.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c8/08/SAM-15-433.PMC9438941.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40349011","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

Multi-scale affinities with missing data: Estimation and applications. 有缺失数据的多尺度亲和力：估计与应用

IF 2.1 4区数学

Statistical Analysis and Data Mining Pub Date : 2022-06-01 Epub Date: 2021-11-05 DOI: 10.1002/sam.11561

Min Zhang, Gal Mishne, Eric C Chi

引用次数: 0