28 citations · 50 across the 10 of their papers we have counts for
10 papers
Bootstrap inference in functional linear regression models with scalar response under heteroscedasticity
Hyemin Yeon, Xiongtao Dai, Daniel John Nordman
Inference for functional linear models in the presence of heteroscedastic errors has received insufficient attention given its practical importance; in fact, even a central limit t…
Predictive Distributions and the Transition from Sparse to Dense Functional Data
Álvaro Gajardo, Xiongtao Dai, Hans-Georg Müller
A representation of Gaussian distributed sparsely sampled longitudinal data in terms of predictive distributions for their functional principal component scores (FPCs) maps availab…
Tukey's Depth for Object Data
Xiongtao Dai, Sara Lopez-Pintado
We develop a novel exploratory tool for non-Euclidean object data based on data depth, extending the celebrated Tukey's depth for Euclidean data. The proposed metric halfspace dept…
A Geospatial Functional Model For OCO-2 Data with Application on Imputation and Land Fraction Estimation
Xinyue Chang, Zhengyuan Zhu, Xiongtao Dai +1
Data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite is essential to many carbon management strategies. A retrieval algorithm is used to estimate CO2 concentration usin…
Statistical Inference on the Hilbert Sphere with Application to Random Densities
Xiongtao Dai
The infinite-dimensional Hilbert sphere has been widely employed to model density functions and shapes, extending the finite-dimensional counterpart. We consider the Fré…
Nonparametric Estimation of Repeated Densities with Heterogeneous Sample Sizes
Jiaming Qiu, Xiongtao Dai, Zhengyuan Zhu
We consider the estimation of densities in multiple subpopulations, where the available sample size in each subpopulation greatly varies. This problem occurs in epidemiology, for e…
Modeling Longitudinal Data on Riemannian Manifolds
Xiongtao Dai, Zhenhua Lin, Hans-Georg Müller
When considering functional principal component analysis for sparsely observed longitudinal data that take values on a nonlinear manifold, a major challenge is how to handle the sp…
Derivative Principal Component Analysis for Representing the Time Dynamics of Longitudinal and Functional Data
Xiongtao Dai, Hans-Georg Müller, Wenwen Tao
We propose a nonparametric method to explicitly model and represent the derivatives of smooth underlying trajectories for longitudinal data. This representation is based on a direc…
Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres
Xiongtao Dai, Hans-Georg Müller
Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered for example as movement trajectories on the surface…
Optimal Bayes Classifiers for Functional Data and Density Ratios
Xiongtao Dai, Hans-Georg Müller, Fang Yao
Bayes classifiers for functional data pose a challenge. This is because probability density functions do not exist for functional data. As a consequence, the classical Bayes classi…