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Multiple hypothesis testing, adjusting for latent variables
latent variables hypothesis testing
2015/8/21
In high throughput settings we inspect a great many candidate variables (e.g. genes) searching for associations with a primary variable (e.g.
a phenotype). High throughput hypothesis testing can be m...
A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables
erm structure dynamics macroeconomic
2015/7/23
We describe the joint dynamics of bond yields and macroeconomic variables in a Vector
Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a
term structure mod...
Bayesian inference with latent variables.
Learning Linear Bayesian Networks with Latent Variables
Linear Networks Bayesian Latent Variables
2012/11/23
This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established...
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
DAG maximal ancestral graph Markov equivalence
2012/9/18
It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one repre...
Gaussian Process Structural Equation Models with Latent Variables
Gaussian Process Structural Equation Models Latent Variables
2010/3/11
In a variety of disciplines such as social sciences,
psychology, medicine and economics, the
recorded data are considered to be noisy measurements
of latent variables connected by some
causal stru...
Using Latent Variables to Eliminate Multicollinearity Effect in A Logistic Regression on Risk Factors for Breast Cancer
Multicollinearity Latent Variables Factor Analysis Principal Components Analysis Logistic Regression Breast Cancer
2009/12/11
Background and Objectives: Logistic regression is one of the most widely used generalized linear models for analysis of the relationships between one or more explanatory variables and a categorical re...
Using Latent Variables to Eliminate Multicollinearity Effect in A Logistic Regression on Risk Factors for Breast Cancer
Multicollinearity Latent Variables Factor Analysis Principal Components Analysis Logistic Regression Breast Cancer
2009/12/11
Background and Objectives: Logistic regression is one of the most widely used generalized linear models for analysis of the relationships between one or more explanatory variables and a categorical re...