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Skip to Search Results- 8Kong, Linglong (Mathematical and Statistical Sciences)
- 2Niu, Di (Electrical and Computer Engineering)
- 1Gombay, Edit (Mathematical and Statistical Sciences)
- 1Hu, Yaozhong (Mathematical and Statistical Sciences)
- 1Karunamuni, Rohana (Mathematical and Statistical Sciences)
- 1Mizera, Ivan (Mathematical and Statistical Sciences)
- 2Quantile regression
- 1ASIMPQR
- 1Average quantile regression
- 1B-spline approximations
- 1Bayesian methods
- 1Clinical Frailty Scale
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Fall 2021
Kernel methods are often used for nonlinear regression and classification in machine learning because they are computationally cheap and accurate. Fourier basis and wavelet basis are the bases that can efficiently approximate the kernel functions. In previous research, Bayesian approximate kernel...
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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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Spring 2021
Envelopes, introduced by Cook et al. (2007), encompass a class of methods for increasing efficiency in multivariate analyses without altering traditional objectives. Envelopes have been successfully incorporated to a variety of regression models from generalized linear models to quantile...
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Leveraging Natural language Processing and Machine Learning Techniques to find Frailty Deficits from Clinical Dataset
DownloadSpring 2023
Introduction Frailty is a syndrome that is often associated with aging. It can be identified through specific frailty scales or a comprehensive assessment by a healthcare provider. In Alberta, it appears that there are no specific billing or diagnostic codes for frailty. So, healthcare providers...
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Spring 2020
Natural Language Processing (NLP) and understanding aims to read from unformatted text to accomplish different tasks. As a first step, it is necessary to represent text as a simplified model. Traditionally, Vector Space Model (VSM) is most commonly used, in which text is represented as a bag of...
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Fall 2017
In this thesis, we study the partial quantile regression methods in functional data analysis. In the first part, we propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression...
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Fall 2017
In recent years fake news has become a more serious problem. This is mainly due to the popularity of social networks, search engines and news ag- gregators that propagate fake news. Classifying news as fake is a hard problem. However it is possible to distinguish between fake and real news, by...
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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...