Zobrazeno 1 - 10
of 176
pro vyhledávání: '"He, Kejun"'
Penalized spline estimation of principal components for sparse functional data: rates of convergence
This paper gives a comprehensive treatment of the convergence rates of penalized spline estimators for simultaneously estimating several leading principal component functions, when the functional data is sparsely observed. The penalized spline estima
Externí odkaz:
http://arxiv.org/abs/2402.05438
In many applications, the process of identifying a specific feature of interest often involves testing multiple hypotheses for their joint statistical significance. Examples include mediation analysis which simultaneously examines the existence of th
Externí odkaz:
http://arxiv.org/abs/2304.10866
In this paper, we propose a novel model to analyze serially correlated two-dimensional functional data observed sparsely and irregularly on a domain which may not be a rectangle. Our approach employs a mixed effects model that specifies the principal
Externí odkaz:
http://arxiv.org/abs/2303.04408
Publikováno v:
Technometrics, 65:4, 524-536 (2023)
Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes
Externí odkaz:
http://arxiv.org/abs/2302.08439
This work studies the multi-task functional linear regression models where both the covariates and the unknown regression coefficients (called slope functions) are curves. For slope function estimation, we employ penalized splines to balance bias, va
Externí odkaz:
http://arxiv.org/abs/2211.04874
Functional principal component analysis has become the most important dimension reduction technique in functional data analysis. Based on B-spline approximation, functional principal components (FPCs) can be efficiently estimated by the expectation-m
Externí odkaz:
http://arxiv.org/abs/2211.04784
Clustered effects are often encountered in multiple hypothesis testing of spatial signals. In this paper, we propose a new method, termed \textit{two-dimensional spatial multiple testing} (2d-SMT) procedure, to control the false discovery rate (FDR)
Externí odkaz:
http://arxiv.org/abs/2210.17121
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest, which has not yet been fully explored. In this article, we develop a novel
Externí odkaz:
http://arxiv.org/abs/2210.12832
We consider the problem of causal discovery (structure learning) from heterogeneous observational data. Most existing methods assume a homogeneous sampling scheme, which leads to misleading conclusions when violated in many applications. To this end,
Externí odkaz:
http://arxiv.org/abs/2201.12392
Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed
Externí odkaz:
http://arxiv.org/abs/2104.00242