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Skip to Search Results- 4Quantile regression
- 2Data cloning
- 12012/02/24
- 1Asymmetric Laplace distribution
- 1Average quantile regression
- 1B-spline approximations
- 1Frolova, Nadezda
- 1Gao, Jueyu
- 1Hassan, Imran
- 1Krkosek, Martin
- 1Lewis, Mark A.
- 1Peacock, Stephanie J.
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Computation in quantile and composite quantile regression models with or without regularization
DownloadFall 2015
Quantile, composite quantile regression with or without regularization have been widely studied and applied in the high-dimensional model estimation and variable selections. Although the theoretical aspect has been well established, the lack of efficient computation methods and publicly available...
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2012-02-24
Ecological systems are complex. All too often, complex models are fit to ecological data without consideration of whether parameters are estimable. I present a recent example for a parasite transmission model tracking the diffusion of sea lice from salmon farms in coastal British Columbia, fit...
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Fall 2016
There is an increasing interest in extreme value analysis for financial and climate data. Various statistical methods have been developed for estimating extreme value dependence in time series data sets and the field continues to grow. In this work we consider four statistical methods for...
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Fall 2014
Quantile regression supplements the ordinary least squares regression and provides a complete view of a relationship between a response variable and a set of covariates. The quantile regression model does not assume any particular error distribution. It is estimated by minimizing an asymmetric...
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Fall 2014
This thesis develops an efficient quantile-adaptive framework for linear and nonlinear variable screening with high-dimensional heterogeneous data. Inspired by the success of various variable screening methods, especially in the quantile-adaptive framework, we develop a more efficient variable...