Nicole Lazar

Nicole Lazar
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Nicole Lazar
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Statistics - Methodology (2)
 
Statistics - Machine Learning (1)
 
Statistics - Theory (1)
 
Mathematics - Statistics (1)
 
Statistics - Computation (1)

Publications Authored By Nicole Lazar

We propose a new approach that combines multiple non-parametric likelihood-type components to build a data-driven approximation of the true likelihood function. Our approach is built on empirical likelihood, a non-parametric approximation of the likelihood function. We show the asymptotic behaviors of our approach are identical to those seen in empirical likelihood. Read More

Non-parametric methods avoid the problem of having to specify a particular data generating mechanism, but can be computationally intensive, reducing their accessibility for large data problems. Empirical likelihood, a non-parametric approach to the likelihood function, is also limited in application due to the computational demands necessary. We propose a new approach that combines multiple non-parametric likelihood-type components to build a data-driven approximation of the true function. Read More

Over the last decade, large-scale multiple testing has found itself at the forefront of modern data analysis. In many applications data are correlated, so that the observed test statistic used for detecting a non-null case, or signal, at each location in a dataset carries some information about the chances of a true signal at other locations. Brown, Lazar, Datta, Jang, and McDowell (2014) proposed in the neuroimaging context a Bayesian multiple testing model that accounts for the dependence of each volume element on the behavior of its neighbors through a conditional autoregressive (CAR) model. Read More

The likelihood function plays a pivotal role in statistical inference; it is adaptable to a wide range of models and the resultant estimators are known to have good properties. However, these results hinge on correct specification of the data generating mechanism. Many modern problems involve extremely complicated distribution functions, which may be difficult -- if not impossible -- to express explicitly. Read More

Identifying homogeneous subgroups of variables can be challenging in high dimensional data analysis with highly correlated predictors. We propose a new method called Hexagonal Operator for Regression with Shrinkage and Equality Selection, HORSES for short, that simultaneously selects positively correlated variables and identifies them as predictive clusters. This is achieved via a constrained least-squares problem with regularization that consists of a linear combination of an L_1 penalty for the coefficients and another L_1 penalty for pairwise differences of the coefficients. Read More