Penalized Least Squares Methods for Latent Variables Models
نویسندگان
چکیده
In this note, we propose a least squares method with l1 penalty (based on the “Lasso”) to estimate models with latent variables. Our approach addresses the high dimensionality of these models, due to the presence of unknown distribution functions. It builds on a recent proposal by Bunea, Tsybakov, Wegkamp and Barbu (2010, Annals of Statistics) that uses penalized least squares for density estimation. We apply the method to a simple measurement error model. The extension to more general latent variables models raises a number of issues that we briefly discuss. JEL codes: C13, C14.
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