5 citations · 11 across the 11 of their papers we have counts for
12 papers
Analyzing Functional Data with a Mixture of Covariance Structures Using a Curve-Based Sampling Scheme
Yian Yu, Bo Wang, Jian Qing Shi
Motivated by distinct walking patterns in real-world free-living gait data, this paper proposes an innovative curve-based sampling scheme for the analysis of functional data charac…
Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable
Zhanfeng Wang, Xinyu Li, Hao Ding +1
Extrinsic Gaussian process regression methods, such as wrapped Gaussian process, have been developed to analyze manifold data. However, there is a lack of intrinsic Gaussian proces…
Gaussian Process for Functional Data Analysis: The GPFDA Package for R
Evandro Konzen, Yafeng Cheng, Jian Qing Shi
We present and describe the GPFDA package for R. The package provides flexible functionalities for dealing with Gaussian process regression (GPR) models for functional data. Multiv…
Joint Curve Registration and Classification with Two-level Functional Models
Lin Tang, Pengcheng Zeng, Jian Qing Shi +1
Many classification techniques when the data are curves or functions have been recently proposed. However, the presence of misaligned problems in the curves can influence the perfo…
Modeling Function-Valued Processes with Nonseparable and/or Nonstationary Covariance Structure
Evandro Konzen, Jian Qing Shi, Zhanfeng Wang
We discuss a general Bayesian framework on modeling multidimensional function-valued processes by using a Gaussian process or a heavy-tailed process as a prior, enabling us to hand…
Smoothing Spline Semiparametric Density Models
Jian Shi, Jiahui Yu, Anna Liu +1
Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric and semiparametric density estimation methods have been prop…