Statistical inference for the sparse parameter of a partially linear single-index model

We perform inference for the sparse and potentially high-dimensional parametric part of a partially linear single-index model. We construct a desparsified version of a penalized estimator for which asymptotic normality can be proven. This allows us to take the uncertainty associated with the variable selection process into account and to construct confidence intervals for all the components of the parameter.no issnstatus: publishe