UNIFORMLY VALID POST-REGULARIZATION CONFIDENCE REGIONS FOR MANY FUNCTIONAL PARAMETERS IN Z-ESTIMATION FRAMEWORK

Abstract

In this paper, we develop procedures to construct simultaneous confidence bands for $\tilde{p}$ potentially infinite-dimensional parameters after model selection for general moment condition models where $\tilde{p}$ is potentially much larger than the sample size of available data, n. This allows us to cover settings with functional response data where each of the $\tilde{p}$ parameters is a function. The procedure is based on the construction of score functions that satisfy Neyman orthogonality condition approximately. The proposed simultaneous confidence bands rely on uniform central limit theorems for high-dimensional vectors (and not on Donsker arguments as we allow for $\tilde{p}\gg n$ ). To construct the bands, we employ a multiplier bootstrap procedure which is computationally efficient as it only involves resampling the estimated score functions (and does not require resolving the high-dimensional optimization problems). We formally apply the general theory to inference on regression coefficient process in the distribution regression model with a logistic link, where two implementations are analyzed in detail. Simulations and an application to real data are provided to help illustrate the applicability of the results.

Keywords: Inference after model selection; Lasso and Post-Lasso with functional response data; moment condition models with a continuum of target parameters.