Empirical Distributions in Selection Bias Models

Models for Sample Selection Bias

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Sample selection bias in credit scoring models

Recovering from Selection Bias using Marginal Structure in Discrete Models

This paper considers the problem of inferring a discrete joint distribution from a sample subject to selection. Abstractly, we want to identify a distribution p(x,w) from its conditional p(x |w). We introduce new assumptions on the marginal model for p(x), under which generic identification is possible. These assumptions are quite general and can easily be tested; they do not require precise ba...

Removing Epistemological Bias From Empirical Observation of Neural Models

This paper addresses the application of neural network research to a theory of autonomous systems Neural net works while enjoying considerable success in autonomous systems applications have failed to provide a rm the oretical underpinning to neural systems embedded in their natural ecological context This paper proposes a stochastic formulation of such an embedding A neural system derived from...

Efficient Empirical Bayes Variable Selection and Estimation in Linear Models

We propose an empirical Bayes method for variable selection and coefficient estimation in linear regression models. The method is based on a particular hierarchical Bayes formulation, and the empirical Bayes estimator is shown to be closely related to the LASSO estimator. Such a connection allows us to take advantage of the recently developed quick LASSO algorithm to compute the empirical Bayes...