-
作者：Wu, Hau-Tieng; Zhou, Zhou
作者单位：Duke University; Duke University; University of Toronto
摘要：We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by nonstationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect al...
-
作者：Cai, Zhanrui; Lei, Jing; Roeder, Kathryn
作者单位：University of Hong Kong; Carnegie Mellon University
摘要：Test of independence is of fundamental importance in modern data analysis, with broad applications in variable selection, graphical models, and causal inference. When the data is high dimensional and the potential dependence signal is sparse, independence testing becomes very challenging without distributional or structural assumptions. In this article, we propose a general framework for independence testing by first fitting a classifier that distinguishes the joint and product distributions, ...
-
作者：Galvao, Antonio F.; Yoon, Jungmo
作者单位：Michigan State University; Hanyang University
摘要：This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroscedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroscedasticity and autocorrelation consistent (HAC...
-
作者：Qiu, Yumou; Sun, Jiarui; Zhou, Xiao-Hua
作者单位：Peking University; Peking University; Peking University; Peking University
摘要：In many applications, the interest is in treatment effects on random quantities of subjects, where those random quantities are not directly observable but can be estimated based on data from each subject. In this article, we propose a general framework for conducting causal inference in a hierarchical data generation setting. The identifiability of causal parameters of interest is shown under a condition on the biasedness of subject level estimates and an ignorability condition on the treatmen...
-
作者：Cho, Haeran; Maeng, Hyeyoung; Eckley, Idris A.; Fearnhead, Paul
作者单位：University of Bristol; Durham University; Lancaster University
摘要：Vector autoregressive (VAR) models are popularly adopted for modeling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modeling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose a piecewise stationar...
-
作者：Catalano, Marta; Lavenant, Hugo; Lijoi, Antonio; Prunster, Igor
作者单位：Luiss Guido Carli University; Bocconi University; Bocconi University; Bocconi University; Bocconi University
摘要：Optimal transport and Wasserstein distances are flourishing in many scientific fields as a means for comparing and connecting random structures. Here we pioneer the use of an optimal transport distance between Levy measures to solve a statistical problem. Dependent Bayesian nonparametric models provide flexible inference on distinct, yet related, groups of observations. Each component of a vector of random measures models a group of exchangeable observations, while their dependence regulates t...
-
作者：Ye, Ting; Keele, Luke; Hasegawa, Raiden; Small, Dylan S.
作者单位：University of Washington; University of Washington Seattle; University of Pennsylvania; Alphabet Inc.; Google Incorporated
摘要：The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID employs a before and after comparison of the treated and control units to remove bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel trends assumption is v...
-
作者：Han, Sukjin
作者单位：University of Bristol
摘要：Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals (e.g., via previous outcomes and covariates). The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization assumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of shar...
-
作者：Ma, Linquan; Wang, Jixin; Chen, Han; Liu, Lan
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison; Rice University; University of California System; University of California Davis
摘要：The estimation of the central space is at the core of the sufficient dimension reduction (SDR) literature. However, it is well known that the finite-sample estimation suffers from collinearity among predictors. Cook, Helland, and Su proposed the predictor envelope method under linear models that can alleviate the problem by targeting a bigger space-which not only envelopes the central information, but also partitions the predictors by finding an uncorrelated set of material and immaterial pred...
-
作者：Pang, Daolin; Zhao, Hongyu; Wang, Tao
作者单位：Shanghai Jiao Tong University; Yale University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University
摘要：We investigate the relationship between count data that inform the relative abundance of features of a composition, and factors that influence the composition. Our work is motivated from microbiome studies aiming to extract microbial signatures that are predictive of host phenotypes based on data collected from a group of individuals harboring radically different microbial communities. We introduce multinomial Factor Augmented Inverse Regression (FAIR) of the count vector onto response factors...
