Annals of Statistics最新文献_第3页

Extreme value inference for heterogeneous power law data 异构幂律数据的极值推断

1区数学

Annals of Statistics Pub Date : 2023-06-01 DOI: 10.1214/23-aos2294

John H.J. Einmahl, Yi He

引用次数: 0

Inference on the maximal rank of time-varying covariance matrices using high-frequency data 基于高频数据的时变协方差矩阵最大秩的推断

1区数学

Annals of Statistics Pub Date : 2023-04-01 DOI: 10.1214/23-aos2273

Markus Reiss, Lars Winkelmann

引用次数: 0

Optimally tackling covariate shift in RKHS-based nonparametric regression 基于rkhs的非参数回归中协变量移位的优化处理

1区数学

Annals of Statistics Pub Date : 2023-04-01 DOI: 10.1214/23-aos2268

Cong Ma, Reese Pathak, Martin J. Wainwright

{"title":"Optimally tackling covariate shift in RKHS-based nonparametric regression","authors":"Cong Ma, Reese Pathak, Martin J. Wainwright","doi":"10.1214/23-aos2268","DOIUrl":"https://doi.org/10.1214/23-aos2268","url":null,"abstract":"We study the covariate shift problem in the context of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We focus on two natural families of covariate shift problems defined using the likelihood ratios between the source and target distributions. When the likelihood ratios are uniformly bounded, we prove that the kernel ridge regression (KRR) estimator with a carefully chosen regularization parameter is minimax rate-optimal (up to a log factor) for a large family of RKHSs with regular kernel eigenvalues. Interestingly, KRR does not require full knowledge of the likelihood ratio apart from an upper bound on it. In striking contrast to the standard statistical setting without covariate shift, we also demonstrate that a naïve estimator, which minimizes the empirical risk over the function class, is strictly suboptimal under covariate shift as compared to KRR. We then address the larger class of covariate shift problems where likelihood ratio is possibly unbounded yet has a finite second moment. Here, we propose a reweighted KRR estimator that weights samples based on a careful truncation of the likelihood ratios. Again, we are able to show that this estimator is minimax optimal, up to logarithmic factors.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135673416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

On high-dimensional Poisson models with measurement error: Hypothesis testing for nonlinear nonconvex optimization. 具有测量误差的高维泊松模型:非线性非凸优化的假设检验。

IF 4.5 1区数学

Annals of Statistics Pub Date : 2023-02-01 DOI: 10.1214/22-aos2248

Fei Jiang, Yeqing Zhou, Jianxuan Liu, Yanyuan Ma

{"title":"On high-dimensional Poisson models with measurement error: Hypothesis testing for nonlinear nonconvex optimization.","authors":"Fei Jiang, Yeqing Zhou, Jianxuan Liu, Yanyuan Ma","doi":"10.1214/22-aos2248","DOIUrl":"https://doi.org/10.1214/22-aos2248","url":null,"abstract":"We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target function. We derive the L1 and L2 convergence rates of the estimator and prove the variable selection consistency. We further establish the asymptotic normality of any subset of the parameters, where the subset can have infinitely many components as long as its cardinality grows sufficiently slow. We develop Wald and score tests based on the asymptotic normality of the estimator, which permits testing of linear functions of the members if the subset. We examine the finite sample performance of the proposed tests by extensive simulation. Finally, the proposed method is successfully applied to the Alzheimer's Disease Neuroimaging Initiative study, which motivated this work initially.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"51 1","pages":"233-259"},"PeriodicalIF":4.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10438917/pdf/nihms-1868138.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10054730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

On High dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization 具有测量误差的高维泊松模型:非线性非凸优化的假设检验

IF 4.5 1区数学

Annals of Statistics Pub Date : 2022-12-31 DOI: 10.48550/arXiv.2301.00139

Fei Jiang, Yeqing Zhou, Jianxuan Liu, Yanyuan Ma

{"title":"On High dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization","authors":"Fei Jiang, Yeqing Zhou, Jianxuan Liu, Yanyuan Ma","doi":"10.48550/arXiv.2301.00139","DOIUrl":"https://doi.org/10.48550/arXiv.2301.00139","url":null,"abstract":"We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target function. We derive the L1 and L2 convergence rates of the estimator and prove the variable selection consistency. We further establish the asymptotic normality of any subset of the parameters, where the subset can have infinitely many components as long as its cardinality grows sufficiently slow. We develop Wald and score tests based on the asymptotic normality of the estimator, which permits testing of linear functions of the members if the subset. We examine the finite sample performance of the proposed tests by extensive simulation. Finally, the proposed method is successfully applied to the Alzheimer's Disease Neuroimaging Initiative study, which motivated this work initially.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"51 1 1","pages":"233-259"},"PeriodicalIF":4.5,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45193479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES. 平均奖励马尔可夫决策过程中的批量策略学习。

IF 4.5 1区数学

Annals of Statistics Pub Date : 2022-12-01 DOI: 10.1214/22-aos2231

Peng Liao, Zhengling Qi, Runzhe Wan, Predrag Klasnja, Susan A Murphy

引用次数: 55

LINEAR BIOMARKER COMBINATION FOR CONSTRAINED CLASSIFICATION. 约束分类的线性生物标志物组合。

IF 4.5 1区数学

Annals of Statistics Pub Date : 2022-10-01 Epub Date: 2022-10-27 DOI: 10.1214/22-aos2210

Yijian Huang, Martin G Sanda

{"title":"LINEAR BIOMARKER COMBINATION FOR CONSTRAINED CLASSIFICATION.","authors":"Yijian Huang, Martin G Sanda","doi":"10.1214/22-aos2210","DOIUrl":"https://doi.org/10.1214/22-aos2210","url":null,"abstract":"Multiple biomarkers are often combined to improve disease diagnosis. The uniformly optimal combination, i.e., with respect to all reasonable performance metrics, unfortunately requires excessive distributional modeling, to which the estimation can be sensitive. An alternative strategy is rather to pursue local optimality with respect to a specific performance metric. Nevertheless, existing methods may not target clinical utility of the intended medical test, which usually needs to operate above a certain sensitivity or specificity level, or do not have their statistical properties well studied and understood. In this article, we develop and investigate a linear combination method to maximize the clinical utility empirically for such a constrained classification. The combination coefficient is shown to have cube root asymptotics. The convergence rate and limiting distribution of the predictive performance are subsequently established, exhibiting robustness of the method in comparison with others. An algorithm with sound statistical justification is devised for efficient and high-quality computation. Simulations corroborate the theoretical results, and demonstrate good statistical and computational performance. Illustration with a clinical study on aggressive prostate cancer detection is provided.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"50 5","pages":"2793-2815"},"PeriodicalIF":4.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635489/pdf/nihms-1819429.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40449706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 1

Quality of life and surgical outcomes of robotic retromuscular ventral hernia repair using a new hybrid mesh reinforcement. 使用新型混合网片加固的机器人腹股沟疝修补术的生活质量和手术效果。

IF 2.3 1区数学

Annals of Statistics Pub Date : 2022-06-01 Epub Date: 2022-04-28 DOI: 10.1007/s10029-022-02619-5

Omar Yusef Kudsi, Georges Kaoukabani, Naseem Bou-Ayash, Kelly Vallar, Alexandra Chudner, Sara LaGrange, Fahri Gokcal

{"title":"Quality of life and surgical outcomes of robotic retromuscular ventral hernia repair using a new hybrid mesh reinforcement.","authors":"Omar Yusef Kudsi, Georges Kaoukabani, Naseem Bou-Ayash, Kelly Vallar, Alexandra Chudner, Sara LaGrange, Fahri Gokcal","doi":"10.1007/s10029-022-02619-5","DOIUrl":"10.1007/s10029-022-02619-5","url":null,"abstract":"Purpose: The purpose of this study is to prospectively evaluate surgical and quality of life (QoL) outcomes of robotic retromuscular ventral hernia repair (rRMVHR) using a new hybrid mesh in high-risk patients.Methods: Data was prospectively collected for patients classified as high-risk based on the modified ventral hernia working group (VHWG) grading system, who underwent rRMVHR using Synecor™ Pre hybrid mesh in a single center, between 2019 and 2020. Pre-, intra- and postoperative variables including hernia recurrence, surgical site events (SSE), hernia-specific quality of life (QoL), and financial costs were analyzed. QoL assessments were obtained from preoperative and postoperative patient visits. Kaplan-Meier survival analysis was performed to analyze the estimated recurrence-free time.Results: Fifty-two high-risk patients, with a mean (±SD) age of 58.6 ± 13.7 years and BMI of 36.9 ± 6.6 kg/m2, were followed for a mean (±SD) period of 22.4 ± 7.1 months. A total of 11 (21.2%) patients experienced postoperative complications, out of which eight were SSEs, including 7 (13.5%) seromas, 1 (1.9%) hematoma, and no infections. Procedural interventions were required for 2 (3.8%) surgical site occurrences. Recurrence was seen in 1 (1.9%) patient. The estimated mean (95% confidence interval) recurrence-free time was 33 (32.3-34.5) months. Postoperative QoL assessments demonstrated significant improvements in comparison to preoperative QoL, with a minimum ∆mean (±SD) of -15.5 ± 2.2 at one month (p < 0.001). The mean (±SD) procedure cost was $13,924.18 ± 7856.95 which includes the average mesh cost ($5390.12 ± 3817.03).Conclusion: Our study showed favorable early and mid-term outcomes, in addition to significant improvements in QoL, after rRMVHR using Synecor™ hybrid mesh in high-risk patients.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"20 1","pages":"881-888"},"PeriodicalIF":2.3,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88522489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING. 双重去偏套索:隐藏混杂下的高维推理。

IF 4.5 1区数学

Annals of Statistics Pub Date : 2022-06-01 Epub Date: 2022-06-16 DOI: 10.1214/21-aos2152

Zijian Guo, Domagoj Ćevid, Peter Bühlmann

引用次数: 23

OPTIMAL FALSE DISCOVERY RATE CONTROL FOR LARGE SCALE MULTIPLE TESTING WITH AUXILIARY INFORMATION. 基于辅助信息的大规模多重测试的最优错误发现率控制。

IF 4.5 1区数学

Annals of Statistics Pub Date : 2022-04-01 DOI: 10.1214/21-aos2128

Hongyuan Cao, Jun Chen, Xianyang Zhang

{"title":"OPTIMAL FALSE DISCOVERY RATE CONTROL FOR LARGE SCALE MULTIPLE TESTING WITH AUXILIARY INFORMATION.","authors":"Hongyuan Cao, Jun Chen, Xianyang Zhang","doi":"10.1214/21-aos2128","DOIUrl":"https://doi.org/10.1214/21-aos2128","url":null,"abstract":"Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust EM algorithm to estimate the prior probabilities of being null and the distribution of p-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"50 2","pages":"807-857"},"PeriodicalIF":4.5,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153594/pdf/nihms-1840915.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9776938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 15