4. Use the sampling distribution of the estimates thus computed to be an approximation to the 'true', population sampling distribution. The plot below contains kernel density plots of the two parameters in the model, as estimated from 10 000 **bootstrap** samples.

Package‘**bootstrap**’ January 2, 2012 Version 1.0-22 Date 2007-09-26 Title Functions for the Book ‘‘An Introduction to the **Bootstrap**’’ Author S original, from StatLib, by Rob Tibshirani.

14.1 TheBootstrap Idea 14.2 FirstStepsin Usingthe **Bootstrap** 14.3 HowAccurate Isa **Bootstrap** Distribution? 14.4 **Bootstrap** Confidence Intervals

Short Guides to Microeconometrics Fall 2010 Unversitat Pompeu Fabra Kurt Schmidheiny The **Bootstrap** 1 Introduction The **bootstrap** is a method to derive properties (standard errors, con

21 Chapter 4 The Original **Bootstrap** Method As shown in the previous chapter, the basic samples of data needed to calculate the confidence intervals have distributions which depart from the traditional parametric distributions.

THE **BOOTSTRAP** by Joel L. Horowitz Department of Economics University of Iowa Iowa City, IA 52242 November 2000 ABSTRACT The **bootstrap** is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data.

Paper 193-29 **Bootstrap** 101: Obtain Robust Confidence Intervals For Any Statistic Dave P. Miller, Ovation Research Group, San Francisco, CA ABSTRACT

**Bootstrap ping** Regression Models Appendix to An Rand S-PLUS Companion to Applied Regression John Fox January 2002 1 Basic Ideas **Bootstrapping** is a general approach to statistical inference based on buildinga sampling distribution for a statistic by resampling from the data at hand.

History of resampling techniques •1949 -Quenouilleproposed the jackknife technique to estimate bias •1958 -Tukeynamed the technique the "jackknife"and used it to estimate standard errors •1979 -B. Efronpublished extensively on the **bootstrap** technique

The following is the ANALYZE macro, modified to **bootstrap** a 95%CI around a median value for the variable''normscr1'' (NOSIC score for rater 1): %macro analyze ...