Statistical Analysis and Data Mining最新文献

{"title":"eRPCA: Robust Principal Component Analysis for Exponential Family Distributions","authors":"Xiaojun Zheng, Simon Mak, Liyan Xie, Yao Xie","doi":"10.1002/sam.11670","DOIUrl":"https://doi.org/10.1002/sam.11670","url":null,"abstract":"Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"71 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312815","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}

{"title":"Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach","authors":"Raydonal Ospina, Ranah Duarte Costa, Leandro Chaves Rêgo, Fernando Marmolejo‐Ramos","doi":"10.1002/sam.11673","DOIUrl":"https://doi.org/10.1002/sam.11673","url":null,"abstract":"This work explores the use of nonparametric quantifiers in the signature verification problem of handwritten signatures. We used the MCYT‐100 (MCYT Fingerprint subcorpus) database, widely used in signature verification problems. The discrete‐time sequence positions in the <jats:italic>x</jats:italic> ‐axis and <jats:italic>y</jats:italic>‐axis provided in the database are preprocessed, and time causal information based on nonparametric quantifiers such as entropy, complexity, Fisher information, and trend are employed. The study also proposes to evaluate these quantifiers with the time series obtained, applying the first and second derivatives of each sequence position to evaluate the dynamic behavior by looking at their velocity and acceleration regimes, respectively. The signatures in the MCYT‐100 database are classified via Logistic Regression, Support Vector Machines (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The quantifiers were used as input features to train the classifiers. To assess the ability and impact of nonparametric quantifiers to distinguish forgery and genuine signatures, we used variable selection criteria, such as: information gain, analysis of variance, and variance inflation factor. The performance of classifiers was evaluated by measures of classification error such as specificity and area under the curve. The results show that the SVM and XGBoost classifiers present the best performance.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"13 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312731","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}

{"title":"Online learning for streaming data classification in nonstationary environments","authors":"Yujie Gai, Kang Meng, Xiaodi Wang","doi":"10.1002/sam.11669","DOIUrl":"https://doi.org/10.1002/sam.11669","url":null,"abstract":"In this article, we implement the classification of nonstationary streaming data. Due to the inability to obtain full data in the context of streaming data, we adopt a strategy based on clustering structure for data classification. Specifically, this strategy involves dynamically maintaining clustering structures to update the model, thereby updating the objective function for classification. Simultaneously, incoming samples are monitored in real-time to identify the emergence of new classes or the presence of outliers. Moreover, this strategy can also deal with the concept drift problem, where the distribution of data changes with the inflow of data. Regarding the handling of novel instances, we introduce a buffer analysis mechanism to delay their processing, which in turn improves the prediction performance of the model. In the process of model updating, we also introduce a novel renewable strategy for the covariance matrix. Numerical simulations and experiments on datasets show that our method has significant advantages.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"36 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070400","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}

{"title":"Error-controlled feature selection for ultrahigh-dimensional and highly correlated feature space using deep learning","authors":"Arkaprabha Ganguli, Tapabrata Maiti, David Todem","doi":"10.1002/sam.11664","DOIUrl":"https://doi.org/10.1002/sam.11664","url":null,"abstract":"Deep learning has been at the center of analytics in recent years due to its impressive empirical success in analyzing complex data objects. Despite this success, most existing tools behave like black-box machines, thus the increasing interest in interpretable, reliable, and robust deep learning models applicable to a broad class of applications. Feature-selected deep learning has emerged as a promising tool in this realm. However, the recent developments do not accommodate ultrahigh-dimensional and highly correlated features or high noise levels. In this article, we propose a novel screening and cleaning method with the aid of deep learning for a data-adaptive multi-resolutional discovery of highly correlated predictors with a controlled error rate. Extensive empirical evaluations over a wide range of simulated scenarios and several real datasets demonstrate the effectiveness of the proposed method in achieving high power while keeping the false discovery rate at a minimum.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"272 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140046612","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}

{"title":"Marginal clustered multistate models for longitudinal progressive processes with informative cluster size","authors":"Sean Xinyang Feng, Aya A. Mitani","doi":"10.1002/sam.11668","DOIUrl":"https://doi.org/10.1002/sam.11668","url":null,"abstract":"Informative cluster size (ICS) is a phenomenon where cluster size is related to the outcome. While multistate models can be applied to characterize the unit‐level transition process for clustered interval‐censored data, there is a research gap addressing ICS within this framework. We propose two extensions of multistate model that account for ICS to make marginal inference: one by incorporating within‐cluster resampling and another by constructing cluster‐weighted score functions. We evaluate the performances of the proposed methods through simulation studies and apply them to the Veterans Affairs Dental Longitudinal Study (VADLS) to understand the effect of risk factors on periodontal disease progression. ICS occurs frequently in dental data, particularly in the study of periodontal disease, as people with fewer teeth due to the disease are more susceptible to disease progression. According to the simulation results, the mean estimates of the parameters obtained from the proposed methods are close to the true values, but methods that ignore ICS can lead to substantial bias. Our proposed methods for clustered multistate model are able to appropriately take ICS into account when making marginal inference of a typical unit from a randomly sampled cluster.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"13 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140033736","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}

{"title":"A novel two‐step extrapolation‐insertion risk model based on the Expectile under the Pareto‐type distribution","authors":"Ziwen Geng","doi":"10.1002/sam.11665","DOIUrl":"https://doi.org/10.1002/sam.11665","url":null,"abstract":"The catastrophe loss model developed is a challenging problem in the insurance industry. In the context of Pareto‐type distribution, measuring risk at the extreme right tail has become a major focus for academic research. The quantile and Expectile of distribution are found to be useful descriptors of its tail, in the same way as the median and mean are related to its central behavior. In this article, a novel two‐step extrapolation‐insertion method is introduced and proved its advantages of less bias and variance theoretically through asymptotic normality by modifying the existing far‐right tail numerical model using the risk measures of Expectile and Expected Shortfall (ES). In addition, another solution to obtain the ES is proposed based on the fitted extreme distribution, which is demonstrated to have superior unbiased statistical properties. Uniting these two methods provides the numerical interval upper and lower bounds for capturing the real quantile‐based ES commonly used in insurance. The numerical simulation and the empirical analysis results of Danish reinsurance claim data indicate that these methods offer high prediction accuracy in the applications of catastrophe risk management.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"37 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953467","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}

{"title":"Bayesian inference for nonprobability samples with nonignorable missingness","authors":"Zhan Liu, Xuesong Chen, Ruohan Li, Lanbao Hou","doi":"10.1002/sam.11667","DOIUrl":"https://doi.org/10.1002/sam.11667","url":null,"abstract":"Nonprobability samples, especially web survey data, have been available in many different fields. However, nonprobability samples suffer from selection bias, which will yield biased estimates. Moreover, missingness, especially nonignorable missingness, may also be encountered in nonprobability samples. Thus, it is a challenging task to make inference from nonprobability samples with nonignorable missingness. In this article, we propose a Bayesian approach to infer the population based on nonprobability samples with nonignorable missingness. In our method, different Logistic regression models are employed to estimate the selection probabilities and the response probabilities; the superpopulation model is used to explain the relationship between the study variable and covariates. Further, Bayesian and approximate Bayesian methods are proposed to estimate the response model parameters and the superpopulation model parameters, respectively. Specifically, the estimating functions for the response model parameters and superpopulation model parameters are utilized to derive the approximate posterior distribution in superpopulation model estimation. Simulation studies are conducted to investigate the finite sample performance of the proposed method. The data from the Pew Research Center and the Behavioral Risk Factor Surveillance System are used to show better performance of our proposed method over the other approaches.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"22 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953469","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}

{"title":"Modeling matrix variate time series via hidden Markov models with skewed emissions","authors":"Michael P. B. Gallaugher, Xuwen Zhu","doi":"10.1002/sam.11666","DOIUrl":"https://doi.org/10.1002/sam.11666","url":null,"abstract":"Data collected today have increasingly become more complex and cannot be analyzed using regular statistical methods. Matrix variate time series data is one such example where the observations in the time series are matrices. Herein, we introduce a set of three hidden Markov models using skewed matrix variate emission distributions for modeling matrix variate time series data. Compared to the hidden Markov model with matrix variate normal emissions, the proposed models present greater flexibility and are capable of modeling skewness in time series data. Parameter estimation is performed using an expectation maximization algorithm. We then look at both simulated data and salary data for public Texas universities.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"37 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956631","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}

{"title":"Subsampling under distributional constraints","authors":"Florian Combes, Ricardo Fraiman, Badih Ghattas","doi":"10.1002/sam.11661","DOIUrl":"https://doi.org/10.1002/sam.11661","url":null,"abstract":"Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input &lt;mjx-container aria-label=\"upper X\" ctxtmenu_counter=\"0\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper X\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml aria-hidden=\"true\" display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"/cms/asset/7d0e6166-8cd5-42ea-ba28-4c46d5e0ba78/sam11661-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper X\" data-semantic-type=\"identifier\"&gt;X&lt;/mi&gt;&lt;/mrow&gt;$$ X $$&lt;/annotation&gt;&lt;/semantics&gt;&lt;/math&gt;&lt;/mjx-assistive-mml&gt;&lt;/mjx-container&gt; in a general space, and an output &lt;mjx-container aria-label=\"upper Y equals f left parenthesis upper X right parenthesis\" ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"0,8\" data-semantic-content=\"1\" data-semantic- data-semantic-role=\"equality\" data-semantic-speech=\"upper Y equals f left parenthesis upper X right parenthesis\" data-semantic-type=\"relseq\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"relseq,=\" data-semantic-parent=\"9\" data-semantic-role=\"equality\" data-semantic-type=\"relation\" rspace=\"5\" space=\"5\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mrow data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"2,6\" data-semantic-content=\"7,2\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"simple function\" data-semantic-type=\"appl\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-operator=\"appl\" data-semantic-parent=\"8\" data-semantic-role=\"simple function\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"appl\" data-semantic-parent=\"8\" data-semantic-role=\"application\" data-semantic-type=\"punctuation\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mrow data-semantic-children=\"4\" data-semantic-content=\"3,5\" data-semantic- data-semantic-parent=\"8\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"6\"","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"36 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769428","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}

{"title":"Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams","authors":"Zahra Nouri, Vahid Kiani, Hamid Fadishei","doi":"10.1002/sam.11662","DOIUrl":"https://doi.org/10.1002/sam.11662","url":null,"abstract":"Today's ever-increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non-static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance-specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average-accuracy of 0.69 on the real-world data streams, while the chunk-based algorithms AWE, AUE, and KUE achieve average-accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average-accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"247 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769619","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}