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The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Paul D. McNicholas, Sanjeena Subedi, Xiaojian Yang, Xiaojian Yang, Xiaojian Yang, Zeny Z. Feng, Zeny Z. Feng, Paul D. McNicholas, Sanjeena Subedi, Sanjeena Subedi, Zeny Z. Feng
Issue Date:March 2012
pp. 629-636
Recent work concerning quantitative traits of interest has focused on selecting a small subset of single nucleotide polymorphisms (SNPs) from among the SNPs responsible for the phenotypic variation of the trait. When considered as covariates, the large num...
     
Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ryan P. Browne,Paul D. McNicholas,Matthew D. Sparling
Issue Date:April 2012
pp. 814-817
We introduce a mixture model whereby each mixture component is itself a mixture of a multivariate Gaussian distribution and a multivariate uniform distribution. Although this model could be used for model-based clustering (model-based unsupervised learning...
 
The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Zeny Z. Feng,Xiaojian Yang,Sanjeena Subedi,Paul D. McNicholas
Issue Date:March 2012
pp. 629-636
Recent work concerning quantitative traits of interest has focused on selecting a small subset of single nucleotide polymorphisms (SNPs) from among the SNPs responsible for the phenotypic variation of the trait. When considered as covariates, the large num...
 
Mixtures of Shifted Asymmetric Laplace Distributions
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Brian C. Franczak,Ryan P. Browne,Paul D. McNicholas
Issue Date:November 2013
pp. 1
A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the generalized inverse Gaussian distrib...
 
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