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Transcription factor-pathway co-expression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma
coexpression cell cycle arrest multiple myeloma hyperdiploid
2016/1/25
Multiple myeloma is a hematological cancer of plasma B cells and remains incurable. Two major subtypes of myeloma, hyperdiploid MM (HMM) and non-hyperdiploid MM (NHMM), have distinct chromosomal alter...
Transcription factor-pathway co-expression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma
coexpression cell cycle arrest multiple myeloma hyperdiploid
2016/1/20
Multiple myeloma is a hematological cancer of plasma B cells and remains incurable. Two major subtypes of myeloma, hyperdiploid MM (HMM) and non-hyperdiploid MM (NHMM), have distinct chromosomal alter...
Factor profiling for ultra high dimensional variable selection
Bayesian Information Criterion Factor Profiling Forward Re- gression Maximum Eigenvalue Ratio Criterion Profiled Independent Screening
2016/1/19
We propose here a novel method of factor profiling (FP) for ultra high dimen-sional variable selection. The new method assumes that the correlation structure of the high dimensional data can be well r...
Analysis of elliptical copula correlation factor model with Kendall's tau
Elliptical copula correlation matrix factor model Kendall’s tau nuclear norm penalty oracle inequality primal-dual certificate
2013/6/14
We study a factor model for the correlation matrix $\Sigma\in\RR^{d\times d}$ of an elliptical copula. The correlations are connected to Kendall's tau and a natural estimation procedure is to plug-in ...
In this short paper, we introduce a mixture of skew-t factor analyzers as well as a family of mixture models based thereon. The mixture of skew-t distributions model that we use arises as a limiting c...
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
Factor analysis topic model personalized learning machine learning block coordinate descent
2013/6/14
Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently developed SPARse Fact...
Sparse Factor Analysis for Learning and Content Analytics
factor analysis sparse probit regression sparse logistic regression Bayesian latent factor analysis personalized learning
2013/4/28
We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain, and content analytics, which estimate the...
Assessing the public health relevance of a risk factor
Risk factor prevalence disease incidence relative risk population-attributable risk c-index
2013/4/27
In a recent series of high impact public health publications, the c-index was used as measure of prediction to assess the public health relevance of a risk factor. I demonstrate that the c-index is an...
Clustering and Classification via Cluster-Weighted Factor Analyzers
Cluster-weighted models factor analysis mixturemodels parsimonious models
2012/11/23
In model-based clustering and classification, the cluster-weighted model constitutes a convenient approach when the random vector of interest constitutes a response variable Y and a set p of explanato...
Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood
High dimensionality unknown factors principal components sparse matrix conditional sparse thresholding cross-sectional correlation penalized maximum likelihood adaptive lasso heteroskedasticity
2012/11/23
We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis ...
Assessing the Health of Richibucto Estuary with the Latent Health Factor Index
AMBI Bayesian statistics hierarchical modelling infaunal trophic index Markov chain Monte Carlo statistical inference
2012/9/17
The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon...
A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix
Correlated factors Inverse Wishart Markov chain Monte Carlo
2011/7/5
Our article considers a regression model with observed factors. The observed factors have a flexible stochastic volatility structure that has separate dynamics for the volatilities and the correlation...
Calibration and filtering for multi factor commodity models with seasonality: incorporating panel data from futures contracts
Multi-Factor Commodity Spot Price Stochastic Volatility Milstein Adaptive Markov chain Monte Carlo Particle filter Rao-Blackwellization
2011/6/21
We examine a general multi-factor model for commodity spot prices and futures valuation. We extend
the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both...
High Dimensional Covariance Matrix Estimation in Approximate Factor Models
sparse estimation thresholding cross-sectional correlation common factors idiosyncratic seemingly unrelated regression
2011/6/20
The variance covariance matrix plays a central role in the inferential theories
of high dimensional factor models in finance and economics. Popular
regularization methods of directly exploiting spar...
Order-preserving factor analysis (OPFA)
Order-preserving factor OPFA precedence-ordering optimization
2011/6/17
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering...