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.
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Showing 1 to 14 of 14 for “"Group Lasso"”.
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Sparse group sufficient dimension reduction and covariance cumulative slicing estimation
… parts: In Part One, for regression problems with grouped covariates, we adopt the idea of sparse group lasso (Friedman et al., 2010) to the framework of the sufficient dimension reduction. We propose a method called the sparse group sufficient dimension reduction (sgSDR) to conduct group and …
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Optimization Algorithms for Structured Machine Learning and Image Processing Problems
… types, including supervised learning (e.g., the group lasso), unsupervised learning (e.g., robust tensor decompositions), and total-variation image denoising. These algorithms are of wide interest to the optimization, machine learning, and image processing communities. Specifically, (i) we …
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Essays in Financial Econometrics
… Models with Invalid Moment Conditions—the Sparse Group Lasso Approach primarily focuses on the GMM estimation of dynamic panel data models, where many moment conditions have been proposed under various assumptions. These moment conditions grow quadratically with the number of time periods T, …
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Robust methods for analyzing multivariate responses with application to time-course data
… number of methods have been developed including Lasso. The group Lasso is an extension of the Lasso with the goal of selecting important groups of variables rather than individual variables. In the third part of the dissertation, we propose two robust group Lasso algorithms for the multivariate …
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Varying Coefficients in Logistic Regression with Applications to Marketing Research
… procedure for model selection that uses a group LASSO penalty to decide which are informative and which variables need varying coefficients in the model.
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Simultaneous estimation approaches to large-scale multivariate regression
… problem. Some popular statistical methods are group lasso and multivariate ridge regression. Most existing methods either leverage the information of the error covariance matrix or assume specific parameter structures. However, in practice, this information is not available. To resolve these …
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High-Dimensional Functional Graphs and Inference for Unknown Heterogeneous Populations
… mixtures of functional regressions, employing a group lasso penalty for variable selection in heterogeneous functional data. Lastly, we recognize the limitations of existing methods in testing the equality of multiple functional graphs and develop a novel, permutation-based testing procedure. …
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Communication-efficient and privacy-preserving statistical learning with applications in high dimensional data
… layer” is then pruned using a federated group lasso adaptation, enabling effective and efficient feature selection on informative learned embeddings rather than raw inputs. Collectively, this dissertation contributes a suite of novel algorithms and theoretical frameworks that …
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Essays on International Finance and Currency Economics
… tail risk in the FX market. Shrinkage method of group LASSO also selects macroeconomic fundamentals and financial variables to have consistent impacts on FX market uncertainties. Besides the standard linear analyses, we adopt the neural network method to examine the non-linear association between …
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Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency
… such as the best subset selection and the Lasso, often do not take the underlying data generation mechanism into considerations. This thesis proposal aims to develop statistical modeling methodologies with a focus on the structured variable selection towards better model interpretation and …
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Semiparametric Characteristics-based Models of Asset Returns
… unknown functions, they are solved by LASSO-style selection model and power enhanced hypothesis tests. The details of the three chapters are summarized below: Specification LASSO and an Application in Financial Markets This chapter proposes the method of Specification-LASSO in a …
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STATISTICAL MODELING FOR COMPLEX FUNCTIONAL AND NETWORK TIME SERIES DATA
… the CFPC method. Last, we proposed a Sparse Group Network AutoRegressive (SGNAR) model to describe the dynamic dependence structure of network. All the proposed models were driven by real data, where the implementation results illustrated promising performance.
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Statistical models with diverging dimensionality
Nowadays in many statistical applications, we face models whose complexity increases with the sample size. Such models pose a challenge to the traditional statistical analysis, and call for new methodologies and new asymptotic studies, which are exactly the focus of my thesis. In particular, my …