Statistical Analysis and Data Mining最新文献_第5页

Imputed quantile vector autoregressive model for multivariate spatial–temporal data 多变量时空数据的估算量级向量自回归模型

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2024-01-25 DOI: 10.1002/sam.11658

Liang Jinwen, Tian Maozai

引用次数: 0

Nonparametric Bayesian functional clustering with applications to racial disparities in breast cancer 非参数贝叶斯功能聚类在乳腺癌种族差异中的应用

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2024-01-25 DOI: 10.1002/sam.11657

Wenyu Gao, Inyoung Kim, Wonil Nam, Xiang Ren, Wei Zhou, Masoud Agah

{"title":"Nonparametric Bayesian functional clustering with applications to racial disparities in breast cancer","authors":"Wenyu Gao, Inyoung Kim, Wonil Nam, Xiang Ren, Wei Zhou, Masoud Agah","doi":"10.1002/sam.11657","DOIUrl":"https://doi.org/10.1002/sam.11657","url":null,"abstract":"As we have easier access to massive data sets, functional analyses have gained more interest. However, such data sets often contain large heterogeneities, noises, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This paper considers noisy information reduction in functional analyses from two perspectives: functional clustering to group similar observations and thus reduce the sample size and functional variable selection to reduce the dimensionality. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this paper proposes a nonparametric Bayesian functional clustering and peak point selection method via weighted Dirichlet process mixture (WDPM) modeling that automatically clusters and provides accurate estimations, together with conditional Laplace prior, which is a conjugate variable selection prior. The proposed method is named WDPM-VS for short, and is able to simultaneously perform the following tasks: (1) Automatic cluster without specifying the number of clusters or cluster centers beforehand; (2) Cluster for heterogeneously behaved functions; (3) Select vibrational peak points; and (4) Reduce noisy information from the two perspectives: sample size and dimensionality. The method will greatly outperform its comparison methods in root mean squared errors. Based on this proposed method, we are able to identify biological factors that can explain the breast cancer racial disparities.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139581738","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

Study of a bounded interval perks distribution with quantile regression analysis 利用量子回归分析研究有界区间津贴分布

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2024-01-25 DOI: 10.1002/sam.11656

Laila A. Al-Essa, Shakaiba Shafiq, Deniz Ozonur, Farrukh Jamal

{"title":"Study of a bounded interval perks distribution with quantile regression analysis","authors":"Laila A. Al-Essa, Shakaiba Shafiq, Deniz Ozonur, Farrukh Jamal","doi":"10.1002/sam.11656","DOIUrl":"https://doi.org/10.1002/sam.11656","url":null,"abstract":"In this article, a novel bounded interval model called the unit-Perks model is developed by suitably transforming the positive random variable of the Perks distribution. Numerous statistical features of the bounded interval Perks model are being explored based on the expansion of the density function. Eight distinct estimation approaches are being used to estimate the parameters of the unit-Perks model. A throughout simulation analysis is also included to evaluate the precision of the resulting estimators from eight estimating approaches. Two real bounded interval data sets are being utilized to investigate the practical applicability of the unit-Perks model. A comparison is also made to determine which method of estimation works better for the given model. According to a comparison of eight different estimation approaches, the maximum likelihood estimation approach outperformed than the other seven estimating approaches. The unit-perks model is then used to introduce the quantile regression model named as quantile unit-Perks distribution. Application to real data set for the quantile unit-Perks distribution is also performed. The quantile residuals are used for the residual analysis of the fitted regression model. On the basis of mathematical, computational, and pictorial evidences, it is concluded that the presented model exhibited greater modeling capabilities.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139581983","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

Boosting diversity in regression ensembles 提升回归集合的多样性

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-12-30 DOI: 10.1002/sam.11654

Mathias Bourel, Jairo Cugliari, Yannig Goude, Jean-Michel Poggi

{"title":"Boosting diversity in regression ensembles","authors":"Mathias Bourel, Jairo Cugliari, Yannig Goude, Jean-Michel Poggi","doi":"10.1002/sam.11654","DOIUrl":"https://doi.org/10.1002/sam.11654","url":null,"abstract":"Ensemble methods, such as Bagging, Boosting, or Random Forests, often enhance the prediction performance of single learners on both classification and regression tasks. In the context of regression, we propose a gradient boosting-based algorithm incorporating a diversity term with the aim of constructing different learners that enrich the ensemble while achieving a trade-off of some individual optimality for global enhancement. Verifying the hypotheses of Biau and Cadre's theorem (2021, <i>Advances in contemporary statistics and econometrics—Festschrift in honour of Christine Thomas-Agnan</i>, Springer), we present a convergence result ensuring that the associated optimization strategy reaches the global optimum. In the experiments, we consider a variety of different base learners with increasing complexity: stumps, regression trees, Purely Random Forests, and Breiman's Random Forests. Finally, we consider simulated and benchmark datasets and a real-world electricity demand dataset to show, by means of numerical experiments, the suitability of our procedure by examining the behavior not only of the final or the aggregated predictor but also of the whole generated sequence.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063502","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

Multivariate contaminated normal mixture regression modeling of longitudinal data based on joint mean-covariance model 基于联合均值-协方差模型的纵向数据多变量污染正态混合回归建模

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-12-22 DOI: 10.1002/sam.11653

Niu Xiaoyu, Tian Yuzhu, Tang Manlai, Tian Maozai

{"title":"Multivariate contaminated normal mixture regression modeling of longitudinal data based on joint mean-covariance model","authors":"Niu Xiaoyu, Tian Yuzhu, Tang Manlai, Tian Maozai","doi":"10.1002/sam.11653","DOIUrl":"https://doi.org/10.1002/sam.11653","url":null,"abstract":"Outliers are common in longitudinal data analysis, and the multivariate contaminated normal (MCN) distribution in model-based clustering is often used to detect outliers and provide robust parameter estimates in each subgroup. In this paper, we propose a method, the mixture of MCN (MCNM), based on the joint mean-covariance model, specifically designed to analyze longitudinal data characterized by mild outliers. Our model can automatically detect outliers in longitudinal data and provide robust parameter estimates in each subgroup. We use iteratively expectation-conditional maximization (ECM) algorithm and Aitken acceleration to estimate the model parameters, achieving both algorithm acceleration and stable convergence. Our proposed method simultaneously clusters the population, identifies progression patterns of the mean and covariance structures for different subgroups over time, and detects outliers. To demonstrate the effectiveness of our method, we conduct simulation studies under various cases involving different proportions and degrees of contamination. Additionally, we apply our method to real data on the number of people infected with AIDS in 49 countries or regions from 2001 to 2021. Results show that our proposed method effectively clusters the data based on various mean progression trajectories. In summary, our proposed MCNM based on the joint mean-covariance model and MCD of covariance matrices provides a robust method for clustering longitudinal data with mild outliers. It effectively detects outliers and identifies progression patterns in different groups over time, making it valuable for various applications in longitudinal data analysis.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139031070","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 machine learning oracle for parameter estimation 用于参数估计的机器学习算法

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-12-09 DOI: 10.1002/sam.11651

Lucas Koepke, Mary Gregg, Michael Frey

{"title":"A machine learning oracle for parameter estimation","authors":"Lucas Koepke, Mary Gregg, Michael Frey","doi":"10.1002/sam.11651","DOIUrl":"https://doi.org/10.1002/sam.11651","url":null,"abstract":"Competing procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning-based decision rule, an “oracle,” can be constructed in such cases to decide the best procedure from a set <math altimg=\"urn:x-wiley:19321864:media:sam11651:sam11651-math-0001\" display=\"inline\" location=\"graphic/sam11651-math-0001.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"script\">C</mi>\u0000</mrow>\u0000$$ mathcal{C} $$</annotation>\u0000</semantics></math> of available preparatory procedures. The oracle learns the decision regions associated with <math altimg=\"urn:x-wiley:19321864:media:sam11651:sam11651-math-0002\" display=\"inline\" location=\"graphic/sam11651-math-0002.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"script\">C</mi>\u0000</mrow>\u0000$$ mathcal{C} $$</annotation>\u0000</semantics></math> based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two-linear-segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138561157","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

The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO 广义双曲线族和通过多选 LASSO 自动选择模型

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-12-08 DOI: 10.1002/sam.11652

Luca Bagnato, Alessio Farcomeni, Antonio Punzo

引用次数: 0

Modeling subpopulations for hierarchically structured data 为分层结构数据建模子种群

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-11-22 DOI: 10.1002/sam.11650

Andrew Simpson, Semhar Michael, Dylan Borchert, Christopher Saunders, Larry Tang

引用次数: 0

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-11-22 DOI: 10.1002/sam.11649

Junsub Jung, Sungil Kim, Heeyoung Kim

{"title":"Spatially-correlated time series clustering using location-dependent Dirichlet process mixture model","authors":"Junsub Jung, Sungil Kim, Heeyoung Kim","doi":"10.1002/sam.11649","DOIUrl":"https://doi.org/10.1002/sam.11649","url":null,"abstract":"The Dirichlet process mixture (DPM) model has been widely used as a Bayesian nonparametric model for clustering. However, the exchangeability assumption of the Dirichlet process is not valid for clustering spatially correlated time series as these data are indexed spatially and temporally. While analyzing spatially correlated time series, correlations between observations at proximal times and locations must be appropriately considered. In this study, we propose a location-dependent DPM model by extending the traditional DPM model for clustering spatially correlated time series. We model the temporal pattern as an infinite mixture of Gaussian processes while considering spatial dependency using a location-dependent Dirichlet process prior over mixture components. This encourages the assignment of observations from proximal locations to the same cluster. By contrast, because mixture atoms for modeling temporal patterns are shared across space, observations with similar temporal patterns can be still grouped together even if they are located far apart. The proposed model also allows the number of clusters to be automatically determined in the clustering procedure. We validate the proposed model using simulated examples. Moreover, in a real case study, we cluster adjacent roads based on their traffic speed patterns that have changed as a result of a traffic accident occurred in Seoul, South Korea.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517923","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

Input-response space-filling designs incorporating response uncertainty 包含响应不确定性的输入-响应空间填充设计

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2023-11-20 DOI: 10.1002/sam.11648

Xiankui Yang, Lu Lu, Christine M. Anderson-Cook

引用次数: 0