# SnoopingCVNearBd: Snooping-adjusted critical values for estimation near a... In kolesarm/BWSnooping: Confidence invervals robust to bandwidth snooping

## Description

Calculate one- and two-sided critical values c_{1-α}(t;k) for values of t in bwratios based on evaluating the Gaussian process \hat{\mathbb{H}}(h) at ngr values of h in the interval [1/t,1].

## Usage

 1 2 SnoopingCVNearBd(S, T, bwratios, kernel, order, db, ngr, alpha = c(0.1, 0.05, 0.01)) 

## Arguments

 S number of draws of the Gaussian process \hat{\mathbb{H}}(h) T number of draws from a normal distribution in each draw of the Gaussian process bwratios Bandwidth ratios of maximum to minimum bandwidth for which to compute critical values kernel Kernel function k(u) supported on [-1,1] that takes a vector or a matrix as an argument u. order Order of local linear regression db Local distance to boundary, equal to x_{0}/\underline{h}, where x_{0} is point of interest. ngr number of grid points at which to evaluate the Gaussian process alpha A vector of values determining the confidence level 1-α at which to compute critical values

kolesarm/BWSnooping documentation built on June 20, 2018, 3:44 p.m.