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 7 of 7 for “"High-dimensional regression"”.
-
Statistical Methods for Large Complex Datasets
… half of this thesis we present methods for high-dimensional regression in the `large p small n' setting for datasets that contain measurement errors or change points.
-
Using prior-data conflict to tune Bayesian regularized regression models
In high-dimensional regression models, variable selection becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective methods for variable selection in p > n scenarios provided the …
-
Statistical Methods for Complex and/or High Dimensional Data
… and implementation of statistical methods for high-dimensional and/or complex data, with an emphasis on $p$, the number of explanatory variables, larger than $n$, the number of observations, the ratio of $p/n$ tending to a finite number, and data with outlier observations. First, we propose a …
-
Bayesian Nonparametric Modeling and Theory for Complex Data
… associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite dimensional object' can refer to a density, a conditional density, a regression surface or even a manifold. Although Bayesian density estimation as well as function …
-
Spatial Coupling for High-Dimensional Estimation
… for a variety of inference problems. For many high-dimensional regression models with unstructured designs, the Bayes-optimal estimator is computa- tionally intractable. The main idea in spatial coupling is to chain simple, unstructured measurement schemes together to obtain significant gains …
-
Semiparametric and Nonparametric Methods for Complex Data
… stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple …