Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Huang, Hsin Hsiung"'
We propose a novel Fr\'echet sufficient dimension reduction (SDR) method based on kernel distance covariance, tailored for metric space-valued responses such as count data, probability densities, and other complex structures. The method leverages a k
Externí odkaz:
http://arxiv.org/abs/2412.13122
Positron Emission Tomography (PET) is a crucial tool in medical imaging, particularly for diagnosing diseases like cancer and Alzheimer's. The advent of Positronium Lifetime Imaging (PLI) has opened new avenues for assessing the tissue micro-environm
Externí odkaz:
http://arxiv.org/abs/2403.14994
Autor:
Huang, Hsin-Hsiung, He, Qing
Nonlinear regression analysis is a popular and important tool for scientists and engineers. In this article, we introduce theories and methods of nonlinear regression and its statistical inferences using the frequentist and Bayesian statistical model
Externí odkaz:
http://arxiv.org/abs/2402.05342
Autor:
He, Qing, Huang, Hsin-Hsiung
Publikováno v:
Journal of Statistical Planning and Inference (2024). 229, 106098
Spatiotemporal data analysis with massive zeros is widely used in many areas such as epidemiology and public health. We use a Bayesian framework to fit zero-inflated negative binomial models and employ a set of latent variables from P\'olya-Gamma dis
Externí odkaz:
http://arxiv.org/abs/2402.04345
We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using $\alpha$-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on the predictors, the central subspace is effecti
Externí odkaz:
http://arxiv.org/abs/2402.00778
Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is therefore crucial to conduct appropriate statistical methods to address
Externí odkaz:
http://arxiv.org/abs/2310.18527
Positron emission tomography (PET) is an important modality for diagnosing diseases such as cancer and Alzheimer's disease, capable of revealing the uptake of radiolabeled molecules that target specific pathological markers of the diseases. Recently,
Externí odkaz:
http://arxiv.org/abs/2206.06463
While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued
Externí odkaz:
http://arxiv.org/abs/2205.07106
We introduce a Bayesian framework for mixed-type multivariate regression using continuous shrinkage priors. Our framework enables joint analysis of mixed continuous and discrete outcomes and facilitates variable selection from the $p$ covariates. The
Externí odkaz:
http://arxiv.org/abs/2201.12839
In real-world application scenarios, it is crucial for marine navigators and security analysts to predict vessel movement trajectories at sea based on the Automated Identification System (AIS) data in a given time span. This article presents an unsup
Externí odkaz:
http://arxiv.org/abs/2007.13712