Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 6 of 6 for “"High-dimensional Linear Regression"”.
-
High-dimensional Linear Regression Problems via Graphical Models
… thesis introduces a new method for solving the linear regression problem where the number of observations n is smaller than the number of variables (predictors) v. In contrast to existing methods such as ridge regression, Lasso and Lars, the proposed method uses the idea of graphical models and …
-
Optimal and Safe Semi-supervised Estimation and Inference for High-dimensional Linear Regression
… these scenarios. In this work, we consider the linear regression problem with a semi-supervised learning data structure under high dimensionality. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation and inference of the regression parameters in …
-
Computational and statistical challenges in high dimensional statistical models
This thesis focuses on two long-studied high-dimensional statistical models, namely (1) the high-dimensional linear regression (HDLR) model, where the goal is to recover a hidden vector of coefficients from noisy linear observations, and (2) the planted clique (PC) model, where the goal is to …
-
Structured Sparsity Promoting Functions: Theory and Applications
… concave penalty based variable selection in high-dimensional linear regression, we introduce a simple scheme to construct structured semiconvex sparsity promoting functions from convex sparsity promoting functions and their Moreau envelopes. Properties of these functions are developed by …
-
Bayesian sparsity learning with variational automatic relevance determination
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms
-
Computationally Efficient Methods for High-Dimensional Statistical Problems
… chapter we address the problem of modelling a high-dimensional linear regression with categorical predictor variables. The natural sparsity assumption in this setting is on the number of unique values the coefficients within each categorical variable can take. With this assumption, we introduce …