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Poisson mean vector estimation with nonparametric maximum likelihood estimation and application to protein domain data 非参数极大似然估计的泊松均值向量估计及其在蛋白质结构域数据中的应用
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2029
Hoyoung Park, Junyong Park
{"title":"Poisson mean vector estimation with nonparametric maximum likelihood estimation and application to protein domain data","authors":"Hoyoung Park, Junyong Park","doi":"10.1214/22-ejs2029","DOIUrl":"https://doi.org/10.1214/22-ejs2029","url":null,"abstract":"","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46150414","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
Estimating the number of communities by spectral methods 用谱方法估计群落数量
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1971
Can M. Le, E. Levina
{"title":"Estimating the number of communities by spectral methods","authors":"Can M. Le, E. Levina","doi":"10.1214/21-ejs1971","DOIUrl":"https://doi.org/10.1214/21-ejs1971","url":null,"abstract":"Community detection is a fundamental problem in network analysis with many methods available to estimate communities. Most of these methods assume that the number of communities is known, which is often not the case in practice. We study a simple and very fast method for estimating the number of communities based on the spectral properties of certain graph operators, such as the non-backtracking matrix and the Bethe Hessian matrix. We show that the method performs well under several models and a wide range of parameters, and is guaranteed to be consistent under several asymptotic regimes. We compare this method to several existing methods for estimating the number of communities and show that it is both more accurate and more computationally efficient. MSC2020 subject classifications: Primary 62H12; secondary 62H30.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46011126","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: 14
Nested covariance functions on graphs with Euclidean edges cross time 具有欧几里得边的图上的嵌套协方差函数
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2039
E. Porcu, X. Emery, A. Peron
{"title":"Nested covariance functions on graphs with Euclidean edges cross time","authors":"E. Porcu, X. Emery, A. Peron","doi":"10.1214/22-ejs2039","DOIUrl":"https://doi.org/10.1214/22-ejs2039","url":null,"abstract":"","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43993257","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
Robust consistent estimators for ROC curves with covariates 带有协变量的ROC曲线的稳健一致估计
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2042
Ana M. Bianco, G. Boente, W. González-Manteiga
{"title":"Robust consistent estimators for ROC curves with covariates","authors":"Ana M. Bianco, G. Boente, W. González-Manteiga","doi":"10.1214/22-ejs2042","DOIUrl":"https://doi.org/10.1214/22-ejs2042","url":null,"abstract":"","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42756461","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: 1
A general framework for tensor screening through smoothing 通过平滑进行张量筛选的一般框架
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1954
Keqian Min, Qing Mai
{"title":"A general framework for tensor screening through smoothing","authors":"Keqian Min, Qing Mai","doi":"10.1214/21-ejs1954","DOIUrl":"https://doi.org/10.1214/21-ejs1954","url":null,"abstract":"Screening is an important technique for analyzing high-dimensional data. Most screening tools have been developed for vectors and are marginal in the sense that each variable is evaluated individually at a time. Many multi-dimensional arrays (tensors) are generated nowadays. In addition to being high-dimensional, these data further have the tensor structure that should be exploited for more efficient analysis. Variables adjacent to each other in a tensor tend to be important or unimportant at the same time. Such information is ignored by marginal screening methods. In this article, we propose a general framework for tensor screening called smoothed tensor screening (STS). STS combines the strength of current marginal screening methods with tensor structural information by aggregating the information of its adjacent variables when evaluating one variable. STS is widely applicable since the statistical utility used in screening can be chosen based on the underlying model or data type of the responses and predictors. Moreover, we establish the SURE screening property for STS under mild conditions. Numerical studies demonstrate that STS has better performance than marginal screening methods. MSC2020 subject classifications: 62P10, 62F07.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41439926","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
Estimating individualized treatment rules for treatments with hierarchical structure 评估具有层次结构的治疗的个性化治疗规则
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1948
Yiwei Fan, Xiaoling Lu, Junlong Zhao, H. Fu, Yufeng Liu
{"title":"Estimating individualized treatment rules for treatments with hierarchical structure","authors":"Yiwei Fan, Xiaoling Lu, Junlong Zhao, H. Fu, Yufeng Liu","doi":"10.1214/21-ejs1948","DOIUrl":"https://doi.org/10.1214/21-ejs1948","url":null,"abstract":"Precision medicine is an increasingly important area of research. Due to the heterogeneity of individual characteristics, patients may respond differently to treatments. One of the most important goals for precision medicine is to develop individualized treatment rules (ITRs) involving patients’ characteristics directly. As an interesting topic in clinical research, many statistical methods have been developed in recent years to find optimal ITRs. For binary treatments, outcome weighted learning (OWL) was proposed to find a decision function of patient characteristics maximizing the expected clinical outcome. Treatments with hierarchical structure are commonly seen in practice. In hierarchical scenarios, how to estimate ITRs is still unclear. We propose a new framework named hierarchical outcome-weighted angle-based learning (HOAL) to estimate ITRs for treatments with hierarchical structure. Statistical properties including Fisher consistency and convergence rates of the proposed method are presented. Simulations and an application to a type 2 diabetes study under linear and nonlinear learning show the highly competitive performance of our proposed procedure in both numerical accuracy and computational efficiency.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43790857","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
Penalized nonparametric likelihood-based inference for current status data model 当前状态数据模型的基于似然的非参数惩罚推理
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1970
Meiling Hao, Yuanyuan Lin, Kin-Yat Liu, Xingqiu Zhao
{"title":"Penalized nonparametric likelihood-based inference for current status data model","authors":"Meiling Hao, Yuanyuan Lin, Kin-Yat Liu, Xingqiu Zhao","doi":"10.1214/21-ejs1970","DOIUrl":"https://doi.org/10.1214/21-ejs1970","url":null,"abstract":": Deriving the limiting distribution of a nonparametric estimate is rather challenging but of fundamental importance to statistical inference. For the current status data, we study a penalized nonparametric likelihood- based estimator for an unknown cumulative hazard function, and establish the pointwise asymptotic normality of the resulting nonparametric esti- mate. We also propose the penalized likelihood ratio tests for local and global hypotheses, derive their limiting distributions, and study the opti- mality of the global test. Simulation studies show that the proposed method works well compared to the classical likelihood ratio test.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46566031","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
Tensor factorization recommender systems with dependency 具有依赖性的张量因子分解推荐系统
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs1978
Jiuchen Zhang, Yubai Yuan, Annie Qu
{"title":"Tensor factorization recommender systems with dependency","authors":"Jiuchen Zhang, Yubai Yuan, Annie Qu","doi":"10.1214/22-ejs1978","DOIUrl":"https://doi.org/10.1214/22-ejs1978","url":null,"abstract":": Dependency structure in recommender systems has been widely adopted in recent years to improve prediction accuracy. In this paper, we propose an innovative tensor-based recommender system, namely, the Ten- sor Factorization with Dependency (TFD). The proposed method utilizes shared factors to characterize the dependency between different modes, in addition to pairwise additive tensor factorization to integrate information among multiple modes. One advantage of the proposed method is that it provides flexibility for different dependency structures by incorporating shared latent factors. In addition, the proposed method unifies both binary and ordinal ratings in recommender systems. We achieve scalable computation for scarce tensors with high missing rates. In theory, we show the asymptotic consistency of estimators with various loss functions for both binary and ordinal data. Our numerical studies demonstrate that the pro- posed method outperforms the existing methods, especially on prediction accuracy.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42174710","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: 1
Nonparametric regression in nonstandard spaces 非标准空间中的非参数回归
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2056
Christof Schötz
{"title":"Nonparametric regression in nonstandard spaces","authors":"Christof Schötz","doi":"10.1214/22-ejs2056","DOIUrl":"https://doi.org/10.1214/22-ejs2056","url":null,"abstract":"","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47792822","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
Casting vector time series: algorithms for forecasting, imputation, and signal extraction 铸造向量时间序列:算法预测，imputation，和信号提取
IF 1.1 4区 数学
Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2068
T. McElroy
{"title":"Casting vector time series: algorithms for forecasting, imputation, and signal extraction","authors":"T. McElroy","doi":"10.1214/22-ejs2068","DOIUrl":"https://doi.org/10.1214/22-ejs2068","url":null,"abstract":"","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47473755","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
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