LPPOptBW.fit: Optimal bandwidth selection for inference at a point

Description Usage Arguments Value Examples

View source: R/LPP_lp.R

Description

Basic computing engine called by LPPOptBW used to find optimal bandwidth

Usage

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LPPOptBW.fit(d, M, kern = "triangular", opt.criterion, alpha = 0.05,
  beta = 0.8, sclass = "H", order = 1, se.initial = "ROTEHW")

Arguments

d

object of class "LPPData"

M

Bound on second derivative of the conditional mean function.

kern

specifies kernel function used in the local regression. It can either be a string equal to "triangular" (k(u)=(1-|u|)_{+}), "epanechnikov" (k(u)=(3/4)(1-u^2)_{+}), or "uniform" (k(u)= (|u|<1)/2), or else a kernel function.

opt.criterion

Optimality criterion that bandwidth is designed to optimize. It can either be based on exact finite-sample maximum bias and finite-sample estimate of variance, or asymptotic approximations to the bias and variance. The options are:

"MSE"

Finite-sample maximum MSE

"FLCI"

Length of (fixed-length) two-sided confidence intervals.

"OCI"

Given quantile of excess length of one-sided confidence intervals

The finite-sample methods use conditional variance given by sigma2, if supplied. Otherwise, for the purpose of estimating the optimal bandwidth, conditional variance is assumed homoscedastic, and estimated using a nearest neighbor estimator.

alpha

determines confidence level, 1-alpha for constructing/optimizing confidence intervals.

beta

Determines quantile of excess length to optimize, if bandwidth optimizes given quantile of excess length of one-sided confidence intervals.

sclass

Smoothness class, either "T" for Taylor or "H" for Hölder class.

order

Order of local regression 1 for linear, 2 for quadratic.

se.initial

Method for estimating initial variance for computing optimal bandwidth. Ignored if data already contains estimate of variance.

"ROTEHW"

Based on residuals from a local linear regression using a triangular kernel and ROT bandwidth

"ROTdemeaned"

Based on sum of squared deviations of outcome from estimate of intercept in local linear regression with triangular kenrel and ROT bandwidth

Value

a list with the following elements

h

Bandwidth

sigma2

estimate of conditional variance, from d

Examples

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# Lee dataset
d <- LPPData(lee08[lee08$margin>0, ], point=0)
LPPOptBW.fit(d, kern = "uniform", M = 0.1, opt.criterion = "MSE")$h

kolesarm/RDHonest documentation built on April 3, 2018, 11:08 a.m.