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Found 13 papers, 6 papers with code
Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds
This paper considers the problem of regression analysis with random covariance matrix as outcome and Euclidean covariates in the framework of Fr\'echet regression on the Bures-Wasserstein manifold.
Transfer Learning with Random Coefficient Ridge Regression
This paper considers estimation and prediction of random coefficient ridge regression in the setting of transfer learning, where in addition to observations from the target model, source samples from different but possibly related regression models are available.
Transfer Learning for Contextual Multi-armed Bandits
The results quantify the contribution of the data from the source domains for learning in the target domain in the context of nonparametric contextual multi-armed bandits.
Statistical Inference for High-Dimensional Linear Regression with Blockwise Missing Data
Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information.
Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression
Based on the iterative estimators, we further construct debiased estimators and establish their asymptotic normality.
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies.
Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality
This paper considers the estimation and prediction of a high-dimensional linear regression in the setting of transfer learning, using samples from the target model as well as auxiliary samples from different but possibly related regression models.
Information content of high-order associations of the human gut microbiota network
Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system.
Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Driven by a wide range of applications, many principal subspace estimation problems have been studied individually under different structural constraints.
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
Under mild tail assumptions and arbitrarily chosen (working) models for the propensity score (PS) and the outcome regression (OR) estimators, satisfying only some high-level conditions, we establish finite sample performance bounds for the DDR estimator showing its (optimal) $L_2$ error rate to be $\sqrt{s (\log d)/ n}$ when both models are correct, and its consistency and DR properties when only one of them is correct.
IFAA: Robust association identification and Inference For Absolute Abundance in microbiome analyses
The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e. g., stool) can be considered as an approximation of RA in an entire ecosystem (e. g., gut).
Applications
Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
In particular, we assume that the causal structure of the treatment, the confounders, the potential mediators and the response is a (possibly unknown) directed acyclic graph (DAG).
Multi-sample Estimation of Bacterial Composition Matrix in Metagenomics Data
Metagenomics sequencing is routinely applied to quantify bacterial abundances in microbiome studies, where the bacterial composition is estimated based on the sequencing read counts.
Methodology Applications Computation
