This paper develops a novel penalized matrix estimation method for sparse dimension reduction when detecting change points in high-dimensional data. The strategy is to project high-dimensional data onto a low-dimensional subspace without losing any change point information, enabling efficient change point detection within this dimension-reduced subspace. Theoretical analysis establishes the consistency of the proposed matrix estimation and selects consistently the important variables which have change points. Numerical studies on synthetic and several real data sets suggest that the dimension reduction strategy enhances the performance of existing approaches. Additionally, the results showcase the efficiency of the proposed algorithm for selecting important variables in high-dimensional sparse data.
{"title":"A sparse dimension-reduced subspace-based approach for detecting multiple change points in high-dimensional data","authors":"Luoyao Yu , Rongzhu Zhao , Jiaqi Huang , Lixing Zhu , Xuehu Zhu","doi":"10.1016/j.jmva.2025.105594","DOIUrl":"10.1016/j.jmva.2025.105594","url":null,"abstract":"<div><div>This paper develops a novel penalized matrix estimation method for sparse dimension reduction when detecting change points in high-dimensional data. The strategy is to project high-dimensional data onto a low-dimensional subspace without losing any change point information, enabling efficient change point detection within this dimension-reduced subspace. Theoretical analysis establishes the consistency of the proposed matrix estimation and selects consistently the important variables which have change points. Numerical studies on synthetic and several real data sets suggest that the dimension reduction strategy enhances the performance of existing approaches. Additionally, the results showcase the efficiency of the proposed algorithm for selecting important variables in high-dimensional sparse data.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105594"},"PeriodicalIF":1.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.jmva.2025.105593
Shu-Yu Li, Han-Ying Liang
Based on panel data, we explore partially linear varying-coefficient quantile regression with group effects under high dimension and missing observations. Using generalized estimating equations, we construct oracle estimators along with smoothed version for the unknown parameter vector, varying-coefficient functions as well as group effects, and establish their asymptotic normality. In the estimation procedure, the within-subject correlations of the panel data are considered by introducing working correlation matrix. We further investigate variable selection by the SCAD penalty for the parameters, varying-coefficient functions and group identification simultaneously, and discuss oracle properties. Meanwhile, hypothesis tests for the parameter, varying-coefficient functions and group effects are done, asymptotic distributions of the restricted estimators and test statistics under both the null and local alternative hypotheses are analyzed. Also, simulation study and real data analysis are conducted to evaluate the performance of the proposed methods.
{"title":"Subgroup effect quantile regression with high dimensional missing panel data","authors":"Shu-Yu Li, Han-Ying Liang","doi":"10.1016/j.jmva.2025.105593","DOIUrl":"10.1016/j.jmva.2025.105593","url":null,"abstract":"<div><div>Based on panel data, we explore partially linear varying-coefficient quantile regression with group effects under high dimension and missing observations. Using generalized estimating equations, we construct oracle estimators along with smoothed version for the unknown parameter vector, varying-coefficient functions as well as group effects, and establish their asymptotic normality. In the estimation procedure, the within-subject correlations of the panel data are considered by introducing working correlation matrix. We further investigate variable selection by the SCAD penalty for the parameters, varying-coefficient functions and group identification simultaneously, and discuss oracle properties. Meanwhile, hypothesis tests for the parameter, varying-coefficient functions and group effects are done, asymptotic distributions of the restricted estimators and test statistics under both the null and local alternative hypotheses are analyzed. Also, simulation study and real data analysis are conducted to evaluate the performance of the proposed methods.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105593"},"PeriodicalIF":1.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.jmva.2025.105592
Minxuan Wu , Joseph Antonelli , Zhihua Su
In this article, we extend predictor envelope models to settings with multivariate outcomes and multiple, functional predictors. We propose a two-step estimation strategy, which first projects the function onto a finite-dimensional Euclidean space before fitting the model using existing approaches to envelope models. We first develop an estimator under a linear model with continuous outcomes and then extend this procedure to the more general class of generalized linear models, which allow for a variety of outcome types. We provide asymptotic theory for these estimators showing that they are root- consistent and asymptotically normal when the regression coefficient is finite-rank. Additionally we show that consistency can be obtained even when the regression coefficient has rank that grows with the sample size. Extensive simulation studies confirm our theoretical results and show strong prediction performance of the proposed estimators. Additionally, we provide multiple data analyses showing that the proposed approach performs well in real-world settings under a variety of outcome types compared with existing dimension reduction approaches.
{"title":"Envelope-based partial least squares in functional regression","authors":"Minxuan Wu , Joseph Antonelli , Zhihua Su","doi":"10.1016/j.jmva.2025.105592","DOIUrl":"10.1016/j.jmva.2025.105592","url":null,"abstract":"<div><div>In this article, we extend predictor envelope models to settings with multivariate outcomes and multiple, functional predictors. We propose a two-step estimation strategy, which first projects the function onto a finite-dimensional Euclidean space before fitting the model using existing approaches to envelope models. We first develop an estimator under a linear model with continuous outcomes and then extend this procedure to the more general class of generalized linear models, which allow for a variety of outcome types. We provide asymptotic theory for these estimators showing that they are root-<span><math><mi>n</mi></math></span> consistent and asymptotically normal when the regression coefficient is finite-rank. Additionally we show that consistency can be obtained even when the regression coefficient has rank that grows with the sample size. Extensive simulation studies confirm our theoretical results and show strong prediction performance of the proposed estimators. Additionally, we provide multiple data analyses showing that the proposed approach performs well in real-world settings under a variety of outcome types compared with existing dimension reduction approaches.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105592"},"PeriodicalIF":1.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We offer a novel test of mutual independence based on consistent estimates of the area under the Kendall curve. We also present an index of dependence that allows one to measure the mutual dependence of a -dimensional random vector with . The index is based on a -dimensional Kendall process. We discuss a standardized version of our index of dependence that is easy to interpret, and provide an algorithm for its computation. Based on the proposed index of dependence, we exemplify a novel method for searching for patterns in the dependence structure. We evaluate the performance of our procedures via simulation, and apply our methods to a real data set.
{"title":"AUK-based test for mutual independence and an index of mutual dependence","authors":"Georgios Afendras , Marianthi Markatou , Nickos Papantonis","doi":"10.1016/j.jmva.2025.105589","DOIUrl":"10.1016/j.jmva.2025.105589","url":null,"abstract":"<div><div>We offer a novel test of mutual independence based on consistent estimates of the area under the Kendall curve. We also present an index of dependence that allows one to measure the mutual dependence of a <span><math><mi>d</mi></math></span>-dimensional random vector with <span><math><mrow><mi>d</mi><mo>></mo><mn>2</mn></mrow></math></span>. The index is based on a <span><math><mi>d</mi></math></span>-dimensional Kendall process. We discuss a standardized version of our index of dependence that is easy to interpret, and provide an algorithm for its computation. Based on the proposed index of dependence, we exemplify a novel method for searching for patterns in the dependence structure. We evaluate the performance of our procedures via simulation, and apply our methods to a real data set.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105589"},"PeriodicalIF":1.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.jmva.2025.105590
Tetsuya Umino , Kazuyoshi Yata , Makoto Aoshima
Scenarios involving high-dimensional, low-sample-size (HDLSS) data are often encountered in modern scientific fields involving genetic microarrays, medical imaging, and finance, where the number of variables can greatly exceed the number of observations. In such settings, a reliable estimation of cross-covariance structures is essential for understanding relationships between variable sets. However, classical estimators often exhibit severe noise accumulation. To address this issue, in this study, we propose a novel thresholding estimator of the cross-covariance matrix for HDLSS settings. We consider the asymptotic properties of the sample cross-covariance matrix and show that the estimator contains large amounts of noise in the high-dimensional setting, which renders it inconsistent. To solve this problem occurring in high-dimensional settings, we develop a new thresholding estimator based on the automatic sparse estimation methodology and show that the estimator is consistent under mild assumptions. We analyze and evaluate the performance of the proposed estimator based on numerical simulations and actual data analysis. The simulations demonstrate that the method attains consistency without requiring the stringent high-dimensional conditions assumed by existing approaches, and the real-data analysis illustrates its applicability to high-dimensional regression problems, wherein improved parameter estimation enhances prediction accuracy. In conclusion, our findings serve as a theoretically sound tool for cross-covariance estimation in HDLSS contexts, with potential implications for a wide range of high-dimensional data analyses.
{"title":"Automatic sparse estimation of the high-dimensional cross-covariance matrix","authors":"Tetsuya Umino , Kazuyoshi Yata , Makoto Aoshima","doi":"10.1016/j.jmva.2025.105590","DOIUrl":"10.1016/j.jmva.2025.105590","url":null,"abstract":"<div><div>Scenarios involving high-dimensional, low-sample-size (HDLSS) data are often encountered in modern scientific fields involving genetic microarrays, medical imaging, and finance, where the number of variables can greatly exceed the number of observations. In such settings, a reliable estimation of cross-covariance structures is essential for understanding relationships between variable sets. However, classical estimators often exhibit severe noise accumulation. To address this issue, in this study, we propose a novel thresholding estimator of the cross-covariance matrix for HDLSS settings. We consider the asymptotic properties of the sample cross-covariance matrix and show that the estimator contains large amounts of noise in the high-dimensional setting, which renders it inconsistent. To solve this problem occurring in high-dimensional settings, we develop a new thresholding estimator based on the automatic sparse estimation methodology and show that the estimator is consistent under mild assumptions. We analyze and evaluate the performance of the proposed estimator based on numerical simulations and actual data analysis. The simulations demonstrate that the method attains consistency without requiring the stringent high-dimensional conditions assumed by existing approaches, and the real-data analysis illustrates its applicability to high-dimensional regression problems, wherein improved parameter estimation enhances prediction accuracy. In conclusion, our findings serve as a theoretically sound tool for cross-covariance estimation in HDLSS contexts, with potential implications for a wide range of high-dimensional data analyses.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105590"},"PeriodicalIF":1.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a new test for vector correlation in a high-dimensional framework, while accommodating a low-dimensional latent factor model. Our test, built under low-dimensional factor models, distinguishes from previous normal approximation-based tests, which are valid under a weak-spike structure. We propose a modified RV coefficient for high-dimensional data, and show that its null-limiting distributions follow a weighted mixture of chi-square distributions under a high-dimensional asymptotic regime integrated with weak technical conditions. By applying this asymptotic result and estimation theory of the number of factors in a low-dimensional factor model, we propose a new approximation test for vector correlations. We also derive the asymptotic power function for the proposed test. Lastly, we examine the finite sample and dimensional performance of this test using Monte Carlo simulations.
{"title":"Low dimensional factor model-based tests for assessing vector correlation in high-dimensional settings","authors":"Masashi Hyodo , Takahiro Nishiyama , Shoichi Narita","doi":"10.1016/j.jmva.2025.105588","DOIUrl":"10.1016/j.jmva.2025.105588","url":null,"abstract":"<div><div>This study proposes a new test for vector correlation in a high-dimensional framework, while accommodating a low-dimensional latent factor model. Our test, built under low-dimensional factor models, distinguishes from previous normal approximation-based tests, which are valid under a weak-spike structure. We propose a modified RV coefficient for high-dimensional data, and show that its null-limiting distributions follow a weighted mixture of chi-square distributions under a high-dimensional asymptotic regime integrated with weak technical conditions. By applying this asymptotic result and estimation theory of the number of factors in a low-dimensional factor model, we propose a new approximation test for vector correlations. We also derive the asymptotic power function for the proposed test. Lastly, we examine the finite sample and dimensional performance of this test using Monte Carlo simulations.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105588"},"PeriodicalIF":1.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.jmva.2025.105579
Ruru Ma, Shibin Zhang
The distribution of maxima is crucial for simultaneous inference of high dimensional parameters. This paper focuses on the bootstrap approximation to the distribution of maxima in high dimensional time series analysis. We propose two novel approaches, the multiplier subsample bootstrap (MSB) and the empirical subsample bootstrap (ESB), to approximate the distribution of maxima constructed from high dimensional time series. Both approaches utilize block-based subsample statistics to build the bootstrap statistics. The MSB assigns weights to block-based subsample statistics using random elements, while the ESB resamples from them independently and uniformly. Under certain regularity conditions, we establish the asymptotic validity of the two proposed approaches, when the parameter dimension is large or even much larger than the sample size. A simulation study demonstrates that both the MSB and ESB perform well.
{"title":"Multiplier and empirical subsample bootstraps for maxima in high dimensional time series analysis","authors":"Ruru Ma, Shibin Zhang","doi":"10.1016/j.jmva.2025.105579","DOIUrl":"10.1016/j.jmva.2025.105579","url":null,"abstract":"<div><div>The distribution of maxima is crucial for simultaneous inference of high dimensional parameters. This paper focuses on the bootstrap approximation to the distribution of maxima in high dimensional time series analysis. We propose two novel approaches, the multiplier subsample bootstrap (MSB) and the empirical subsample bootstrap (ESB), to approximate the distribution of maxima constructed from high dimensional time series. Both approaches utilize block-based subsample statistics to build the bootstrap statistics. The MSB assigns weights to block-based subsample statistics using random elements, while the ESB resamples from them independently and uniformly. Under certain regularity conditions, we establish the asymptotic validity of the two proposed approaches, when the parameter dimension is large or even much larger than the sample size. A simulation study demonstrates that both the MSB and ESB perform well.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105579"},"PeriodicalIF":1.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1016/j.jmva.2025.105587
Lauri Heinonen, Joni Virta
This work presents sparse invariant coordinate selection, SICS, a new method for sparse and robust independent component analysis. SICS is based on classical invariant coordinate selection, which is presented in such a form that a LASSO-type penalty can be applied to promote sparsity. Robustness is achieved by using robust scatter matrices. In the first part of the paper, the background and building blocks: scatter matrices, measures of robustness, ICS and independent component analysis, are carefully introduced. Then the proposed new method and its algorithm are derived and presented. This part also includes consistency and breakdown point results for a general case of sparse ICS-like methods. The performance of SICS in identifying sparse independent component loadings is investigated with multiple simulations. The method is illustrated with an example in constructing sparse causal graphs and we also propose a graphical tool for selecting the appropriate sparsity level in SICS.
{"title":"A method for sparse and robust independent component analysis","authors":"Lauri Heinonen, Joni Virta","doi":"10.1016/j.jmva.2025.105587","DOIUrl":"10.1016/j.jmva.2025.105587","url":null,"abstract":"<div><div>This work presents sparse invariant coordinate selection, SICS, a new method for sparse and robust independent component analysis. SICS is based on classical invariant coordinate selection, which is presented in such a form that a LASSO-type penalty can be applied to promote sparsity. Robustness is achieved by using robust scatter matrices. In the first part of the paper, the background and building blocks: scatter matrices, measures of robustness, ICS and independent component analysis, are carefully introduced. Then the proposed new method and its algorithm are derived and presented. This part also includes consistency and breakdown point results for a general case of sparse ICS-like methods. The performance of SICS in identifying sparse independent component loadings is investigated with multiple simulations. The method is illustrated with an example in constructing sparse causal graphs and we also propose a graphical tool for selecting the appropriate sparsity level in SICS.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105587"},"PeriodicalIF":1.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.jmva.2025.105578
Jie Wu , Bo Zhang , Daoji Li , Zemin Zheng
Heterogeneous data are now ubiquitous in many applications in which correctly identifying the subgroups from a heterogeneous population is critical. Although there is an increasing body of literature on subgroup detection, existing methods mainly focus on the univariate response setting. In this paper, we propose a joint heterogeneity and reduced-rank learning framework to simultaneously identify the subgroup structure and estimate the covariate effects for heterogeneous multivariate response regression. In particular, our approach uses rank-constrained pairwise fusion penalization and conducts the subgroup analysis without requiring prior knowledge regarding the individual subgroup memberships. We implement the proposed approach by an alternating direction method of multipliers (ADMM) algorithm and show its convergence. We also establish the asymptotic properties for the resulting estimators under mild and interpretable conditions. A predictive information criterion is proposed to select the rank of the coefficient matrix with theoretical support. The effectiveness of the proposed approach is demonstrated through simulation studies and a real data application.
{"title":"Simultaneous heterogeneity and reduced-rank learning for multivariate response regression","authors":"Jie Wu , Bo Zhang , Daoji Li , Zemin Zheng","doi":"10.1016/j.jmva.2025.105578","DOIUrl":"10.1016/j.jmva.2025.105578","url":null,"abstract":"<div><div>Heterogeneous data are now ubiquitous in many applications in which correctly identifying the subgroups from a heterogeneous population is critical. Although there is an increasing body of literature on subgroup detection, existing methods mainly focus on the univariate response setting. In this paper, we propose a joint heterogeneity and reduced-rank learning framework to simultaneously identify the subgroup structure and estimate the covariate effects for heterogeneous multivariate response regression. In particular, our approach uses rank-constrained pairwise fusion penalization and conducts the subgroup analysis without requiring prior knowledge regarding the individual subgroup memberships. We implement the proposed approach by an alternating direction method of multipliers (ADMM) algorithm and show its convergence. We also establish the asymptotic properties for the resulting estimators under mild and interpretable conditions. A predictive information criterion is proposed to select the rank of the coefficient matrix with theoretical support. The effectiveness of the proposed approach is demonstrated through simulation studies and a real data application.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105578"},"PeriodicalIF":1.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.jmva.2025.105563
Hanteng Ma , Peijun Sang , Xingdong Feng , Xin Liu
Functional classification has been increasingly helpful in exploring and predicting a response variable with multiple categories. In fact, both functional and scalar covariates may be useful and should be included in the model simultaneously, and thus developing a robust multi-categorical functional classifier with statistical guarantees is desirable. However, both of these two issues are rarely touched in previous studies. Motivated by these, in this paper we propose a novel large margin linear mixed functional classifier for the response with multiple categories, which includes both functional and scalar covariates as predictors, especially when functional data are sparsely longitudinal. Not only does the proposed method address the functional classification using a combination of both functional and scalar covariates, but also provides a robust multi-categorical mixed functional classifier using a large margin loss adaptive to observed samples. Furthermore, we establish statistical theories of a mixed functional classifier, which have been less considered in existing literature. An efficient algorithm is also proposed for its practical implementation. Numerical investigations have supported the superb performance of the proposed method on both simulated and real datasets.
{"title":"A robust mixed functional classifier with adaptive large margin loss","authors":"Hanteng Ma , Peijun Sang , Xingdong Feng , Xin Liu","doi":"10.1016/j.jmva.2025.105563","DOIUrl":"10.1016/j.jmva.2025.105563","url":null,"abstract":"<div><div>Functional classification has been increasingly helpful in exploring and predicting a response variable with multiple categories. In fact, both functional and scalar covariates may be useful and should be included in the model simultaneously, and thus developing a robust multi-categorical functional classifier with statistical guarantees is desirable. However, both of these two issues are rarely touched in previous studies. Motivated by these, in this paper we propose a novel large margin linear mixed functional classifier for the response with multiple categories, which includes both functional and scalar covariates as predictors, especially when functional data are sparsely longitudinal. Not only does the proposed method address the functional classification using a combination of both functional and scalar covariates, but also provides a robust multi-categorical mixed functional classifier using a large margin loss adaptive to observed samples. Furthermore, we establish statistical theories of a mixed functional classifier, which have been less considered in existing literature. An efficient algorithm is also proposed for its practical implementation. Numerical investigations have supported the superb performance of the proposed method on both simulated and real datasets.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105563"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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