Electronic Journal of Statistics最新文献_第4页

Asymptotic normality of a change plane estimator in fixed dimension with near-optimal rate 具有近最优速率的固定维变换平面估计量的渐近正态性

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2144

Debarghya Mukherjee, Moulinath Banerjee, Debasri Mukherjee, Ya’acov Ritov

{"title":"Asymptotic normality of a change plane estimator in fixed dimension with near-optimal rate","authors":"Debarghya Mukherjee, Moulinath Banerjee, Debasri Mukherjee, Ya’acov Ritov","doi":"10.1214/23-ejs2144","DOIUrl":"https://doi.org/10.1214/23-ejs2144","url":null,"abstract":"Linear thresholding models postulate that the conditional distribution of a response variable in terms of covariates differs on the two sides of a (typically unknown) hyperplane in the covariate space. A key goal in such models is to learn about this separating hyperplane. Exact likelihood or least squares methods to estimate the thresholding parameter involve an indicator function which make them difficult to optimize and are, therefore, often tackled by using a surrogate loss that uses a smooth approximation to the indicator. In this paper, we demonstrate that the resulting estimator is asymptotically normal with a near optimal rate of convergence: n−1 up to a log factor, in both classification and regression thresholding models. This is substantially faster than the currently established convergence rates of smoothed estimators for similar models in the statistics and econometrics literatures. We also present a real-data application of our approach to an environmental data set where CO2 emission is explained in terms of a separating hyperplane defined through per-capita GDP and urban agglomeration.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135954678","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

Testing linear operator constraints in functional response regression with incomplete response functions 不完全响应函数在函数响应回归中的线性算子约束检验

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2177

Yeonjoo Park, Kyunghee Han, Douglas G. Simpson

{"title":"Testing linear operator constraints in functional response regression with incomplete response functions","authors":"Yeonjoo Park, Kyunghee Han, Douglas G. Simpson","doi":"10.1214/23-ejs2177","DOIUrl":"https://doi.org/10.1214/23-ejs2177","url":null,"abstract":"Hypothesis testing procedures are developed to assess linear operator constraints in function-on-scalar regression when incomplete functional responses are observed. The approach enables statistical inferences about the shape and other aspects of the functional regression coefficients within a unified framework encompassing three incomplete sampling scenarios; (i) partially observed response functions as curve segments over random sub-intervals of the domain, (ii) discretely observed functional responses with additive measurement errors, and (iii) the composition of former two scenarios, where partially observed response segments are observed discretely with measurement error. The latter scenario has been little explored to date, although such structured data is increasingly common in applications. For statistical inference, deviations from the constraint space are measured via integrated L2-distance between estimates from the constrained and unconstrained model spaces. Large sample properties of the proposed test procedure are established, including the consistency, asymptotic distribution, and local power of the test statistic. The finite sample power and level of the proposed test are investigated in a simulation study covering a variety of scenarios. The proposed methodologies are illustrated by applications to U.S. obesity prevalence data, analyzing the functional shape of its trends over time, and motion analysis in a study of automotive ergonomics.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135662403","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

Spectrum inference for replicated spatial locally time-harmonizable time series 复制空间局部时间可调和时间序列的谱推断

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2130

J. Aston, D. Dehay, A. Dudek, Jean-Marc Freyermuth, Dénes Szűcs, Lincoln J. Colling

引用次数: 0

Uniform confidence bands for hazard functions from censored prevalent cohort survival data 经审查的流行队列生存数据的危险函数的统一置信带

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2133

Ali Shariati, H. Doosti, V. Fakoor, M. Asgharian

引用次数: 0

Kernel machines with missing covariates 缺少协变量的核机

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2158

Tiantian Liu, Yair Goldberg

引用次数: 0

Reweighted nonparametric likelihood inference for linear functionals 线性泛函的重加权非参数似然推理

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2168

Karun Adusumilli, Taisuke Otsu, Chen Qiu

引用次数: 0

Besov-Laplace priors in density estimation: optimal posterior contraction rates and adaptation 密度估计中的贝索夫-拉普拉斯先验:最佳后验收缩率和适应性

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2161

Matteo Giordano

引用次数: 1

Pretest estimation in combining probability and non-probability samples 结合概率和非概率样本的预测估计

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2137

Chenyin Gao, Shu Yang

{"title":"Pretest estimation in combining probability and non-probability samples","authors":"Chenyin Gao, Shu Yang","doi":"10.1214/23-ejs2137","DOIUrl":"https://doi.org/10.1214/23-ejs2137","url":null,"abstract":"Multiple heterogeneous data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we develop a unified framework of the test-and-pool approach to general parameter estimation by combining gold-standard probability and non-probability samples. We focus on the case when the study variable is observed in both datasets for estimating the target parameters, and each contains other auxiliary variables. Utilizing the probability design, we conduct a pretest procedure to determine the comparability of the non-probability data with the probability data and decide whether or not to leverage the non-probability data in a pooled analysis. When the probability and non-probability data are comparable, our approach combines both data for efficient estimation. Otherwise, we retain only the probability data for estimation. We also characterize the asymptotic distribution of the proposed test-and-pool estimator under a local alternative and provide a data-adaptive procedure to select the critical tuning parameters that target the smallest mean square error of the test-and-pool estimator. Lastly, to deal with the non-regularity of the test-and-pool estimator, we construct a robust confidence interval that has a good finite-sample coverage property.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45146367","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: 2

Asymptotic normality of Gini correlation in high dimension with applications to the K-sample problem 高维基尼相关的渐近正态性及其在k -样本问题中的应用

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2165

Yongli Sang, Xin Dang

引用次数: 1

Selective inference for clustering with unknown variance 未知方差聚类的选择推理

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2143

Y. Yun, R. Barber

{"title":"Selective inference for clustering with unknown variance","authors":"Y. Yun, R. Barber","doi":"10.1214/23-ejs2143","DOIUrl":"https://doi.org/10.1214/23-ejs2143","url":null,"abstract":"In many modern statistical problems, the limited available data must be used both to develop the hypotheses to test, and to test these hypotheses-that is, both for exploratory and confirmatory data analysis. Reusing the same dataset for both exploration and testing can lead to massive selection bias, leading to many false discoveries. Selective inference is a framework that allows for performing valid inference even when the same data is reused for exploration and testing. In this work, we are interested in the problem of selective inference for data clustering, where a clustering procedure is used to hypothesize a separation of the data points into a collection of subgroups, and we then wish to test whether these data-dependent clusters in fact represent meaningful differences within the data. Recent work by Gao et al. [2022] provides a framework for doing selective inference for this setting, where a hierarchical clustering algorithm is used for producing the cluster assignments, which was then extended to k-means clustering by Chen and Witten [2022]. Both these works rely on assuming a known covariance structure for the data, but in practice, the noise level needs to be estimated-and this is particularly challenging when the true cluster structure is unknown. In our work, we extend this work to the setting of noise with unknown variance, and provide a selective inference method for this more general setting. Empirical results show that our new method is better able to maintain high power while controlling Type I error when the true noise level is unknown.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48717813","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: 2