A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression release_gnrbsmdx6jcbvfdm3tdwlazahi

by Prithish Banerjee, Broti Garai, Himel Mallick, Shrabanti Chowdhury, Saptarshi Chatterjee

Published in Journal of Probability and Statistics by Hindawi Limited.

2018   Volume 2018, p1-9

Abstract

We consider the problem of modelling count data with excess zeros using Zero-Inflated Poisson (ZIP) regression. Recently, various regularization methods have been developed for variable selection in ZIP models. Among these, EM LASSO is a popular method for simultaneous variable selection and parameter estimation. However, EM LASSO suffers from estimation inefficiency and selection inconsistency. To remedy these problems, we propose a set of EM adaptive LASSO methods using a variety of data-adaptive weights. We show theoretically that the new methods are able to identify the true model consistently, and the resulting estimators can be as efficient as oracle. The methods are further evaluated through extensive synthetic experiments and applied to a German health care demand dataset.
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Date   2018-12-30
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