Bayesian factor regression models in the''large p

By Matthew West. 2003

547 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/368b5ad48e6de062cd09fc0de433c3685321dbc0

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Citations in the corpus

Bayesian group factor analysis with structured sparsity (2016, background): “One traditional solution is to restrict the loading matrix to be lower triangular (West, 2003; Carvalho et al., 2008).”
Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models (2012, methodology): “For each series of experiments, we generated 10 replicates of this generative model, and we studied the distributions of P -values for tests using LFMMs.”
Local shrinkage rules, Lévy processes and regularized regression (2010, methodology): “Our approach therefore builds on the work of Frank and Friedman (1993), Clyde et al. (1996), Denison and George (2000), West (2003) and Maruyama and George (2010), who provide a unified framework for ridge regression (RR), principal component regression (PCR), partial least squares (PLS), the…”
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning (2011, background): “From a Bayesian perspective, the spike and slab prior [1, 4, 5], also called twogroups prior [6], is the golden standard for sparse linear models.”
Parameterization and Bayesian Modeling (2004, methodology): “They connect somewhat to the recent approach of West (2003) for using linear combinations to include more predictors in a model than data points. The approach of West (2003) is more completely data-based, whereas the class of models we have developed here is structured”
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (2017, background): “This is known as the “large p , small N ” paradigm in statistics [80] where p refers to N el,f = dim( λ f ); • the prohibitive cost of enlarging the data set size N ; and • the high dimension N dof,f = dim( u f ) of the discretized solution/output vector u f .”
Dynamic Stock Selection Strategies: A Structured Factor Model Framework (2012, methodology): “This step is exactly as it appears in West (2003).”
Nonparametric Bayesian Kernel Models (2007, background): “[13] defined and exemplified the use of a flexible and practically very effective class of generalised g-priors that allow for different degrees of shrinkage estimation of regression parameters in different principal component directions on the induced design space for any regression model, and we a”
A review on machine learning principles for multi-view biological data integration (2016, methodology): “96], factor analysis (FA) [97, 98], NMF [99, 100, 101, 102], SR [65] and tensor decomposition methods [103, 104], are commonly used feature extraction models.”
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics (2008, methodology): “Our extensions here represent generalizations of the standard methods for multivariate regression and ANOVA, as well as extensions of the original sparse factor regression model versions of West (2003).”
Nonparametric factor analysis with beta process priors (2009, methodology): “X = ΦZ + E (9) Often, prior knowledge about the structure of the data is used, for example, the desired sparseness properties of the Φ or Z matrices (West, 2003; Rai & Daumé, 2008; Knowles & Ghahramani, 2007).”
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data (2003, background): “, high dimension and small sample size) typical of gene expression data (West, 2002; West et al., 2001).”
Beta-Negative Binomial Process and Poisson Factor Analysis (2011, methodology): “Latent variable models under the Gaussian assumption, such as principal component analysis and factor analysis, are widely used to discover low-dimensional data structure (Lawrence, 2005; Tipping and Bishop, 1999; West, 2003; Zhou et al., 2009).”
BAYESIAN HYPER‐LASSOS WITH NON‐CONVEX PENALIZATION (2011, background): “George & McCulloch (1997), West (2003), Ishwaran & Rao (2005) and Fahrmeir, Kneib & Konrath (2010) explore their use for univariate responses, and Brown, Vannucci & Fearn (1998) consider multivariate ∗Author to whom correspondence should be addressed.”
Estimating brain functional connectivity with sparse multivariate autoregression (2005, unknown): “A different approach has arisen in the data mining context, motivated to a great extent by the demands posed by analysis of micro-array data (West 2002; Efron et al .”
Feature Selection via Dependence Maximization (2012, background): “Finding a global optimum for (1) is typically NP-hard (Weston et al., 2003), unless the criterion is easily decomposable or has properties which make approximate optimization easier, for example, submodularity (Nemhauser et al., 1978; Guestrin et al., 2005).”