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Simultaneous Functional Quantile Regression 同时功能分位数回归
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0248
Boyi Hu, Xixi Hu, Hua Liu, Jinhong You, Jiguo Cao
{"title":"Simultaneous Functional Quantile Regression","authors":"Boyi Hu, Xixi Hu, Hua Liu, Jinhong You, Jiguo Cao","doi":"10.5705/ss.202021.0248","DOIUrl":"https://doi.org/10.5705/ss.202021.0248","url":null,"abstract":"The conventional method for functional quantile regression (FQR) is to fit the regression model for each quantile of interest separately. Therefore, the slope function of the regression, as a bivariate function of time and quantile, is estimated as a univariate function of time for each fixed quantile. However, there are several limitations to this conventional strategy. For example, it cannot guarantee the monotonicity of the conditional quantiles, nor can it control the smoothness of the slope estimator as a bivariate function. In this paper, we propose a new framework for FQR, in which we simultaneously fit the FQR model for multiple quantiles, with the help of a bivariate basis under some constraints, such that the estimated quantiles satisfy the monotonicity conditions and the smoothness of the slope estimator is controlled. The proposed estimator for the slope function is shown to be asymptotically consistent, and we establish its asymptotic normality. We use simulation to evaluate the finite-sample performance of the proposed method and compare it with that of the conventional method. We demonstrate the proposed method by analyzing the effects of Statistica Sinica: Preprint doi:10.5705/ss.202021.0248","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937519","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}
引用次数: 0
Generalized Odds Rate Frailty Models for Current Status Data with Informative Censoring 具有信息过滤的当前状态数据的广义优势率脆弱模型
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0411
Yang Xu, Shishun Zhao, T. Hu, Jianguo Sun
{"title":"Generalized Odds Rate Frailty Models for Current Status Data with Informative Censoring","authors":"Yang Xu, Shishun Zhao, T. Hu, Jianguo Sun","doi":"10.5705/ss.202021.0411","DOIUrl":"https://doi.org/10.5705/ss.202021.0411","url":null,"abstract":": Current-status data occur in many areas, and the analysis of such data attracted much attention. In this study, we consider a regression analysis of current-status data in the presence of informative censoring, for which most existing methods either apply only to limited situations or are computationally unstable. Here, we propose a new sieve maximum likelihood estimation procedure under the class of semiparametric generalized odds rate frailty models. The proposed method uses the latent variable to describe the informative censoring or relationship between the failure time of interest and the censoring time. We develop a novel expectation-maximization algorithm for determining the proposed estimators, and establish their asymptotic consistency and normality. The results of a simulation study show that the proposed method performs well in practical","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938072","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}
引用次数: 0
Statistical Inference for Mean Function of Longitudinal Imaging Data over Complicated Domains 复杂域纵向成像数据均值函数的统计推断
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0415
Qirui Hu, Jie Li
{"title":"Statistical Inference for Mean Function of Longitudinal Imaging Data over Complicated Domains","authors":"Qirui Hu, Jie Li","doi":"10.5705/ss.202021.0415","DOIUrl":"https://doi.org/10.5705/ss.202021.0415","url":null,"abstract":"We propose a novel procedure for estimating the mean function of longitudinal imaging data with inherent spatial and temporal correlation. We depict the dependence between temporally ordered images using a functional moving average, and use flexible bivariate splines over triangulations to handle the irregular domain of images which is common in imaging studies. We establish both the global and the local asymptotic properties of the bivariate spline estimator for the mean function, with simultaneous confidence corridors (SCCs) as a theoretical byproduct. Under some mild conditions, the proposed estimator and its accompanying SCCs are shown to be consistent and oracle efficient, as though all images were entirely observed without errors. We use Monte Carlo simulation experiments to demonstrate the finite-sample performance of the proposed method, the results of which strongly corroborate the asymptotic theory. The proposed method is further illustrated by analyzing two seawater potential temperature data sets.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938100","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}
引用次数: 1
Group Testing Regression Analysis with Missing Data and Imperfect Tests 缺失数据和不完善检验的组检验回归分析
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0382
A. Delaigle, Ruoxu Tan
{"title":"Group Testing Regression Analysis with Missing Data and Imperfect Tests","authors":"A. Delaigle, Ruoxu Tan","doi":"10.5705/ss.202021.0382","DOIUrl":"https://doi.org/10.5705/ss.202021.0382","url":null,"abstract":": Estimating the prevalence of an infectious disease in a big population typically requires testing a specimen (e.g., blood, urine, or swab) for the disease. When the disease spreads quickly, time constraints and limited resources often restrict the number of tests that can be performed. In such cases, if the prevalence is not too high, the group testing procedure can be employed to save time, money, and resources. The procedure tests pooled specimens of groups of individuals, rather than testing each individual for the disease. This technique is also used in other contexts, for example, to detect abnormalities or contamination in animals, plants, food, or water. Although methods exist for estimating a prevalence conditional on the explanatory variables from the group testing data, they require the specimen to be available for all individuals, which is not always possible. Therefore, we construct new nonparametric estimators that are consistent when some of the specimens are missing. We demonstrate the numerical performance of our methods using simulations and a hepatitis B example.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938201","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}
引用次数: 1
A Linear Errors-in-Variables Model with Unknown Heteroscedastic Measurement Errors 具有未知异方差测量误差的线性变量误差模型
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-10-21 DOI: 10.5705/ss.202022.0331
L. Nghiem, Cornelis J. Potgieter
{"title":"A Linear Errors-in-Variables Model with Unknown Heteroscedastic Measurement Errors","authors":"L. Nghiem, Cornelis J. Potgieter","doi":"10.5705/ss.202022.0331","DOIUrl":"https://doi.org/10.5705/ss.202022.0331","url":null,"abstract":"In the classic measurement error framework, covariates are contaminated by independent additive noise. This paper considers parameter estimation in such a linear errors-in-variables model where the unknown measurement error distribution is heteroscedastic across observations. We propose a new generalized method of moment (GMM) estimator that combines a moment correction approach and a phase function-based approach. The former requires distributions to have four finite moments, while the latter relies on covariates having asymmetric distributions. The new estimator is shown to be consistent and asymptotically normal under appropriate regularity conditions. The asymptotic covariance of the estimator is derived, and the estimated standard error is computed using a fast bootstrap procedure. The GMM estimator is demonstrated to have strong finite sample performance in numerical studies, especially when the measurement errors follow non-Gaussian distributions.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139315809","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}
引用次数: 0
NONPARAMETRIC ESTIMATION AND TESTING FOR PANEL COUNT DATA WITH INFORMATIVE TERMINAL EVENT. 具有信息终端事件的面板计数数据的非参数估计和检验。
IF 1.5 3区 数学
Statistica Sinica Pub Date : 2023-10-01 DOI: 10.5705/ss.202021.0213
Xiangbin Hu, Li Liu, Ying Zhang, Xingqiu Zhao
{"title":"NONPARAMETRIC ESTIMATION AND TESTING FOR PANEL COUNT DATA WITH INFORMATIVE TERMINAL EVENT.","authors":"Xiangbin Hu, Li Liu, Ying Zhang, Xingqiu Zhao","doi":"10.5705/ss.202021.0213","DOIUrl":"10.5705/ss.202021.0213","url":null,"abstract":"<p><p>Informative terminal events often occur in the long term recurrent event follow-up studies. To reflect their effects on recurrent event processes explicitly, we propose a reversed nonparametric mean model for panel count data with a terminal event subject to right censoring. This model enjoys meaningful interpretation for applications and robustness for statistical inference. Treating the distribution of the right-censored terminal event time as a nuisance functional parameter, we develop a two-stage estimation procedure by combining the Kaplan-Meier estimator and nonparametric sieve estimation techniques. The consistency, convergence rate and asymptotic normality of the proposed nonparametric estimator are established. Then we construct a class of new statistics for two-sample test. The asymptotic properties of the new tests are established and evaluated by extensive simulation studies. Panel count data from Chinese Longitudinal Healthy Longevity study are analyzed using the proposed method for illustration.</p>","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":"2763-2786"},"PeriodicalIF":1.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12030113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Use of random integration to test equality of high dimensional covariance matrices. 使用随机积分来检验高维协方差矩阵的相等性。
IF 1.5 3区 数学
Statistica Sinica Pub Date : 2023-10-01 DOI: 10.5705/ss.202020.0486
Yunlu Jiang, Canhong Wen, Yukang Jiang, Xueqin Wang, Heping Zhang
{"title":"Use of random integration to test equality of high dimensional covariance matrices.","authors":"Yunlu Jiang, Canhong Wen, Yukang Jiang, Xueqin Wang, Heping Zhang","doi":"10.5705/ss.202020.0486","DOIUrl":"10.5705/ss.202020.0486","url":null,"abstract":"<p><p>Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a general multivariate model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under different settings when there exist a few large or many small diagonal disturbances between the two covariance matrices.</p>","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"33 4","pages":"2359-2380"},"PeriodicalIF":1.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41162333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA. 用半竞争风险数据定义和估计主要地层特定自然中介效应。
IF 1.2 3区 数学
Statistica Sinica Pub Date : 2023-10-01 DOI: 10.5705/ss.202021.0167
Fei Gao, Fan Xia, K C G Chan
{"title":"DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA.","authors":"Fei Gao, Fan Xia, K C G Chan","doi":"10.5705/ss.202021.0167","DOIUrl":"10.5705/ss.202021.0167","url":null,"abstract":"<p><p>In many medical studies, an ultimate failure event, such as death, is likely to be affected by the occurrence and timing of other intermediate clinical events. Both event times are subject to censoring by loss-to-follow-up, but the nonterminal event may be further censored by the occurrence of the primary outcome, but not vice versa. To study the effect of an intervention on both events, the intermediate event may be viewed as a mediator. However, the conventional definitions of direct and indirect effects do not apply, because of the semi-competing risks data structure. We define three principal strata based on whether the potential intermediate event occurs before the potential failure event. This allows us to properly define direct and indirect effects in one stratum, and define total effects for all strata. We discuss the identification conditions for the stratum-specific effects, and propose a semiparametric estimator based on a multivariate logistic stratum membership model and within-stratum proportional hazards models for the event times. By treating the unobserved stratum membership as a latent variable, we propose an expectation-maximization algorithm for the computation. We study the asymptotic properties of the estimators using modern empirical process theory and examine the performance of the estimators in numerical studies.</p>","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":"2495-2517"},"PeriodicalIF":1.2,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Leverage Classifier: Another Look at Support Vector Machine 杠杆分类器:另看支持向量机
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-08-23 DOI: 10.5705/ss.202023.0124
Yixin Han, Jun Yu, Nan Zhang, Cheng Meng, Ping Ma, Wenxuan Zhong, Changliang Zou
{"title":"Leverage Classifier: Another Look at Support Vector Machine","authors":"Yixin Han, Jun Yu, Nan Zhang, Cheng Meng, Ping Ma, Wenxuan Zhong, Changliang Zou","doi":"10.5705/ss.202023.0124","DOIUrl":"https://doi.org/10.5705/ss.202023.0124","url":null,"abstract":"Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale datasets. In this paper, we propose a new optimal leverage classifier based on linear SVM under a nonseparable setting. Our classifier aims to select an informative subset of the training sample to reduce data size, enabling efficient computation while maintaining high accuracy. We take a novel view of SVM under the general subsampling framework and rigorously investigate the statistical properties. We propose a two-step subsampling procedure consisting of a pilot estimation of the optimal subsampling probabilities and a subsampling step to construct the classifier. We develop a new Bahadur representation of the SVM coefficients and derive unconditional asymptotic distribution and optimal subsampling probabilities without giving the full sample. Numerical results demonstrate that our classifiers outperform the existing methods in terms of estimation, computation, and prediction.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48579241","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}
引用次数: 0
An Unbiased Predictor for Skewed Response Variable with Measurement Error in Covariate 具有协变量测量误差的偏态响应变量的无偏预测器
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-08-21 DOI: 10.5705/ss.202023.0098
Sepideh Mosaferi, M. Ghosh, S. Sugasawa
{"title":"An Unbiased Predictor for Skewed Response Variable with Measurement Error in Covariate","authors":"Sepideh Mosaferi, M. Ghosh, S. Sugasawa","doi":"10.5705/ss.202023.0098","DOIUrl":"https://doi.org/10.5705/ss.202023.0098","url":null,"abstract":"We introduce a new small area predictor when the Fay-Herriot normal error model is fitted to a logarithmically transformed response variable, and the covariate is measured with error. This framework has been previously studied by Mosaferi et al. (2023). The empirical predictor given in their manuscript cannot perform uniformly better than the direct estimator. Our proposed predictor in this manuscript is unbiased and can perform uniformly better than the one proposed in Mosaferi et al. (2023). We derive an approximation of the mean squared error (MSE) for the predictor. The prediction intervals based on the MSE suffer from coverage problems. Thus, we propose a non-parametric bootstrap prediction interval which is more accurate. This problem is of great interest in small area applications since statistical agencies and agricultural surveys are often asked to produce estimates of right skewed variables with covariates measured with errors. With Monte Carlo simulation studies and two Census Bureau's data sets, we demonstrate the superiority of our proposed methodology.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47397122","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}
引用次数: 0
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