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Skip to Search Results- 8Kong, Linglong (Mathematical and Statistical Sciences)
- 4Karunamuni, Rohana (Mathematical and Statistical Sciences)
- 2Cribben, Ivor (Finance and Statistical Analysis)
- 2Niu, Di (Electrical and Computer Engineering)
- 1Gombay, Edit (Mathematical and Statistical Sciences)
- 1Hu, Yaozhong (Mathematical and Statistical Sciences)
- 1Akinlawon, Oludotun J
- 1Bazrafkan, Mehrnoosh
- 1Gao, Jueyu
- 1Lewandowski, Alex
- 1Liu, Bang
- 1Mavrin, Borislav
- 2Machine learning
- 2Quantile regression
- 1ASIMPQR
- 1Average quantile regression
- 1B-spline approximations
- 1Bayesian deep learning
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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 2012
Replicated regular two-level factorial experiments are very useful for industry. The basic purpose of this type of experiments is to identify active effects that affect the mean and variance of the response. Hypothesis testing procedures are widely used for this purpose. However, the existing...
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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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Fall 2015
Graphical models are frequently used to explore networks among a set of variables. Several methods for estimating sparse graphs have been proposed and their theoretical properties have been explored. There are also several selection criteria to select the optimal estimated models. However, their...
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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 2018
Gaussian processes are flexible probabilistic models for regression and classification. However, their success hinges on a well-specified kernel that can capture the structure of data. For complex data, the task of hand crafting a kernel becomes daunting. In this thesis, we propose new methods...
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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...