Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer
Journal of the American Statistical Association, 2023 Transfer learning provides a powerful tool for incorporating data from related studies into a target study of interest. In epidemiology and medical studies, the classification of a target disease could borrow information across other related diseases and
Sai Li, Linjun Zhang, T. Cai, Hongzhe Li
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Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers [PDF]
arXiv.org, 2022 Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, and wave propagation.
Marvin Pfortner +3 more
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A robust gene regulatory network inference method base on Kalman filter and linear regression. [PDF]
PLoS ONE, 2018 The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology.
Jamshid Pirgazi, Ali Reza Khanteymoori
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Semisupervised inference for explained variance in high dimensional linear regression and its applications [PDF]
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2018 The paper considers statistical inference for the explained variance βTΣβ under the high dimensional linear model Y=Xβ+ε in the semisupervised setting, where β is the regression vector and Σ is the design covariance matrix.
T. Tony Cai, Zijian Guo
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Prediction-powered inference [PDF]
Science, 2023 Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.
Anastasios Nikolas Angelopoulos +4 more
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Causality inference of linearly correlated variables: The statistical simulation and regression method [PDF]
Computational Ecology and Software, 2021 Causality inference of variables is a research focus in science. Due to its importance, a statistical simulation and regression method for causality inference of linearly correlated (scale or interval) variables was proposed in present study.
WenJun Zhang
doaj
Variational Inference in high-dimensional linear regression
, 2021 39 ...
Mukherjee, Sumit, Sen, Subhabrata
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Regression analysis for covariate‐adaptive randomization: A robust and efficient inference perspective [PDF]
Statistics in Medicine, 2020 Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate‐adaptive ...
Wei Ma, Fuyi Tu, Hanzhong Liu
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Estimation and Inference for Multi-Kink Quantile Regression [PDF]
Journal of business & economic statistics, 2020 This article proposes a new Multi-Kink Quantile Regression (MKQR) model which assumes different linear quantile regression forms in different regions of the domain of the threshold covariate but are still continuous at kink points.
Wei Zhong, Chuang Wan, Wenyang Zhang
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Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river [PDF]
Global Journal of Environmental Science and Management, 2018 : In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve ...
G. Elkiran +3 more
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