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bootstrap R

bootstrap.pdf cran

was recently asked about options for bootstrapping. The following post sets out some applications of bootstrapping and strategies for implementing it in R. I've found bootstrapping useful in several settings:
where the statistic I'm interested in is a little unusual: the average R-square across five separate regressions; the difference in the average correlation of a set of variables between two groups
non parametric statistics, such as the median
when assumptions such as normality of homoscedasticity are not satisfied

Bootstrapping can be a very useful tool in statistics and it is very easily implemented in R. Bootstrapping comes in handy when there is doubt that the usual distributional assumptions and asymptotic results are valid and accurate. Bootstrapping is a nonparametric method which lets us compute estimated standard errors, confidence intervals and hypothesis testing.

Generally bootstrapping follows the same basic steps:

1. Resample a given data set a specified number of times
2. Calculate a specific statistic from each sample
3. Find the standard deviation of the distribution of that statistic

Abstract
In disciplines other than IS, the use of covariance-based structural equation modelling (SEM) is the mainstream
method for SEM analysis, and for confirmatory factor analysis (CFA). Yet a body of IS literature has developed
arguing that PLS regression is a superior tool for these analyses, and for establishing reliability and validity.
Despite these claims, the views underlying this PLS literature are not universally shared. In this paper the
authors review the PLS and mainstream SEM literatures, and describe the key differences between the two
classes of tools. The paper also canvasses why PLS regression is rarely used in management, marketing,
organizational behaviour, and that branch of psychology concerned with good measurement – psychometrics.
The paper offers some practical options to Australasian researchers seeking greater mastery of SEM, and also
acts as a roadmap for readers who want to check for themselves what the mainstream SEM literature has to say.
Keywords
Structural equation modelling, PLS, CFA, reliability

Bootstrap Methods and their
Application
Anthony Davison




c 2006
A short course based on the book
‘Bootstrap Methods and their Application’,
by A. C. Davison and D. V. Hinkley




c Cambridge University

espite several attempts at reading about bootstrapping, I seem to always hit a brick wall. I wonder if anyone can give a reasonably non-technical definition of bootstrapping?

I know it is not possible in this forum to provide enough detail to enable me to fully understand it, but a gentle push in the right direction with the main goal and mechanism of bootstrapping would be much appreciated! Thanks.

Bootstrapping
Nonparametric Bootstrapping
The boot package

Statisticians can reuse their data to quantify the uncertainty of complex models

Bootstrapping Regression Models
Appendix to An R and S-PLUS Companion to Applied Regression
John Fox
January 2002
1 Basic Ideas
Bootstrapping is a general approach to statistical inference based on building a sampling distribution for
a statistic by resampling from the data at hand. The term ‘bootstrapping,’ due to Efron (1979), is an
allusion to the expression ‘pulling oneself up by one’s bootstraps’ – in this case, using the sample data as
a population from which repeated samples are draw