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In statistics, bootstrapping is any test or metric that relies on
random sampling with replacement. Bootstrapping allows assigning
measures of accuracy (defined in terms of bias, variance, confidence
intervals, prediction error or some other such measure) to sample
estimates.[1][2] This technique allows estimation of the sampling
distribution of almost any statistic using random sampling
methods.[3][4] Generally, it falls in the broader class of resampling
methods.
Bootstrapping is the practice of estimating properties of an estimator
(such as its variance) by measuring those properties when sampling
from an approximating distribution. One standard choice for an
approximating distribution is the empirical distribution function of
the observed data. In the case where a set of observations can be
assumed to be from an independent and identically distributed
population, this can be implemented by constructing a number of
resamples with replacement, of the observed dataset (and of equal size
to the observed dataset).
It may also be used for constructing hypothesis tests. It is often
used as an alternative to statistical inference based on the
assumption of a parametric model when that assumption is in doubt, or
where parametric inference is impossible or requires complicated
formulas for the calculation of standard errors.

Contents

1 History 2 Approach 3 Discussion

3.1 Advantages 3.2 Disadvantages 3.3 Recommendations

4 Types of bootstrap scheme

4.1 Case resampling

4.1.1 Estimating the distribution of sample mean 4.1.2 Regression

4.2 Bayesian bootstrap 4.3 Smooth bootstrap 4.4 Parametric bootstrap 4.5 Resampling residuals 4.6 Gaussian process regression bootstrap 4.7 Wild bootstrap 4.8 Block bootstrap

4.8.1 Time series: Simple block bootstrap 4.8.2 Time series: Moving block bootstrap 4.8.3 Cluster data: block bootstrap

5 Choice of statistic 6 Deriving confidence intervals from the bootstrap distribution

6.1 Bias, asymmetry, and confidence intervals 6.2 Methods for bootstrap confidence intervals

7 Example applications

7.1 Smoothed bootstrap

8 Relation to other approaches to inference

8.1 Relationship to other resampling methods 8.2 U-statistics

9 See also 10 References 11 Further reading 12 External links

12.1 Software

History[edit]
The bootstrap was published by
**Bradley Efron** in "Bootstrap methods:
another look at the jackknife" (1979),[5][6][7] inspired by earlier
work on the jackknife.[8][9][10] Improved estimates of the variance
were developed later.[11][12] A Bayesian extension was developed in
1981.[13] The bias-corrected and accelerated (BCa) bootstrap was
developed by Efron in 1987,[14] and the ABC procedure in 1992.[15]
Approach[edit]
The basic idea of bootstrapping is that inference about a population
from sample data, (sample → population), can be modelled by
resampling the sample data and performing inference about a sample
from resampled data, (resampled → sample). As the population is
unknown, the true error in a sample statistic against its population
value is unknown. In bootstrap-resamples, the 'population' is in fact
the sample, and this is known; hence the quality of inference of the
'true' sample from resampled data, (resampled → sample), is
measurable.
More formally, the bootstrap works by treating inference of the true
probability distribution J, given the original data, as being
analogous to inference of the empirical distribution of Ĵ, given the
resampled data. The accuracy of inferences regarding Ĵ using the
resampled data can be assessed because we know Ĵ. If Ĵ is a
reasonable approximation to J, then the quality of inference on J can
in turn be inferred.
As an example, assume we are interested in the average (or mean)
height of people worldwide. We cannot measure all the people in the
global population, so instead we sample only a tiny part of it, and
measure that. Assume the sample is of size N; that is, we measure the
heights of N individuals. From that single sample, only one estimate
of the mean can be obtained. In order to reason about the population,
we need some sense of the variability of the mean that we have
computed. The simplest bootstrap method involves taking the original
data set of N heights, and, using a computer, sampling from it to form
a new sample (called a 'resample' or bootstrap sample) that is also of
size N. The bootstrap sample is taken from the original by using
sampling with replacement (e.g. we might 'resample' 5 times from
[1,2,3,4,5] and get [2,5,4,4,1]), so, assuming N is sufficiently
large, for all practical purposes there is virtually zero probability
that it will be identical to the original "real" sample. This process
is repeated a large number of times (typically 1,000 or 10,000 times),
and for each of these bootstrap samples we compute its mean (each of
these are called bootstrap estimates). We now have a histogram of
bootstrap means. This provides an estimate of the shape of the
distribution of the mean from which we can answer questions about how
much the mean varies. (The method here, described for the mean, can be
applied to almost any other statistic or estimator.)
Discussion[edit]

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Advantages[edit] A great advantage of bootstrap is its simplicity. It is a straightforward way to derive estimates of standard errors and confidence intervals for complex estimators of complex parameters of the distribution, such as percentile points, proportions, odds ratio, and correlation coefficients. Bootstrap is also an appropriate way to control and check the stability of the results. Although for most problems it is impossible to know the true confidence interval, bootstrap is asymptotically more accurate than the standard intervals obtained using sample variance and assumptions of normality.[16] Disadvantages[edit] Although bootstrapping is (under some conditions) asymptotically consistent, it does not provide general finite-sample guarantees. The apparent simplicity may conceal the fact that important assumptions are being made when undertaking the bootstrap analysis (e.g. independence of samples) where these would be more formally stated in other approaches. Recommendations[edit] The number of bootstrap samples recommended in literature has increased as available computing power has increased. If the results may have substantial real-world consequences, then one should use as many samples as is reasonable, given available computing power and time. Increasing the number of samples cannot increase the amount of information in the original data; it can only reduce the effects of random sampling errors which can arise from a bootstrap procedure itself. Adèr et al. recommend the bootstrap procedure for the following situations:[17]

When the theoretical distribution of a statistic of interest is complicated or unknown. Since the bootstrapping procedure is distribution-independent it provides an indirect method to assess the properties of the distribution underlying the sample and the parameters of interest that are derived from this distribution.

When the sample size is insufficient for straightforward statistical inference. If the underlying distribution is well-known, bootstrapping provides a way to account for the distortions caused by the specific sample that may not be fully representative of the population.

When power calculations have to be performed, and a small pilot sample is available. Most power and sample size calculations are heavily dependent on the standard deviation of the statistic of interest. If the estimate used is incorrect, the required sample size will also be wrong. One method to get an impression of the variation of the statistic is to use a small pilot sample and perform bootstrapping on it to get impression of the variance.

However, Athreya has shown[18] that if one performs a naive bootstrap on the sample mean when the underlying population lacks a finite variance (for example, a power law distribution), then the bootstrap distribution will not converge to the same limit as the sample mean. As a result, confidence intervals on the basis of a Monte Carlo simulation of the bootstrap could be misleading. Athreya states that "Unless one is reasonably sure that the underlying distribution is not heavy tailed, one should hesitate to use the naive bootstrap". Types of bootstrap scheme[edit]

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In univariate problems, it is usually acceptable to resample the individual observations with replacement ("case resampling" below) unlike subsampling, in which resampling is without replacement and is valid under much weaker conditions compared to the bootstrap. In small samples, a parametric bootstrap approach might be preferred. For other problems, a smooth bootstrap will likely be preferred. For regression problems, various other alternatives are available.[19] Case resampling[edit] Bootstrap is generally useful for estimating the distribution of a statistic (e.g. mean, variance) without using normal theory (e.g. z-statistic, t-statistic). Bootstrap comes in handy when there is no analytical form or normal theory to help estimate the distribution of the statistics of interest, since bootstrap method can apply to most random quantities, e.g., the ratio of variance and mean. There are at least two ways of performing case resampling.

The Monte Carlo algorithm for case resampling is quite simple. First, we resample the data with replacement, and the size of the resample must be equal to the size of the original data set. Then the statistic of interest is computed from the resample from the first step. We repeat this routine many times to get a more precise estimate of the Bootstrap distribution of the statistic. The 'exact' version for case resampling is similar, but we exhaustively enumerate every possible resample of the data set. This can be computationally expensive as there are a total of

(

2 n − 1

n

)

displaystyle binom 2n-1 n

different resamples, where n is the size of the data set.

Estimating the distribution of sample mean[edit] Consider a coin-flipping experiment. We flip the coin and record whether it lands heads or tails. Let X = x1, x2, …, x10 be 10 observations from the experiment. xi = 1 if the i th flip lands heads, and 0 otherwise. From normal theory, we can use t-statistic to estimate the distribution of the sample mean,

x ¯

=

1 10

(

x

1

+

x

2

+ … +

x

10

)

displaystyle bar x = frac 1 10 (x_ 1 +x_ 2 +ldots +x_ 10 )

. Instead, we use bootstrap, specifically case resampling, to derive the distribution of

x ¯

displaystyle bar x

. We first resample the data to obtain a bootstrap resample. An example of the first resample might look like this X1* = x2, x1, x10, x10, x3, x4, x6, x7, x1, x9. Note that there are some duplicates since a bootstrap resample comes from sampling with replacement from the data. Note also that the number of data points in a bootstrap resample is equal to the number of data points in our original observations. Then we compute the mean of this resample and obtain the first bootstrap mean: μ1*. We repeat this process to obtain the second resample X2* and compute the second bootstrap mean μ2*. If we repeat this 100 times, then we have μ1*, μ2*, …, μ100*. This represents an empirical bootstrap distribution of sample mean. From this empirical distribution, one can derive a bootstrap confidence interval for the purpose of hypothesis testing. Regression[edit] In regression problems, case resampling refers to the simple scheme of resampling individual cases - often rows of a data set. For regression problems, so long as the data set is fairly large, this simple scheme is often acceptable. However, the method is open to criticism[citation needed]. In regression problems, the explanatory variables are often fixed, or at least observed with more control than the response variable. Also, the range of the explanatory variables defines the information available from them. Therefore, to resample cases means that each bootstrap sample will lose some information. As such, alternative bootstrap procedures should be considered. Bayesian bootstrap[edit] Bootstrapping can be interpreted in a Bayesian framework using a scheme that creates new datasets through reweighting the initial data. Given a set of

N

displaystyle N

data points, the weighting assigned to data point

i

displaystyle i

in a new dataset

D

J

displaystyle mathcal D ^ J

is

w

i

J

=

x

i

J

−

x

i − 1

J

displaystyle w_ i ^ J =x_ i ^ J -x_ i-1 ^ J

, where

x

J

displaystyle mathbf x ^ J

is a low-to-high ordered list of

N − 1

displaystyle N-1

uniformly distributed random numbers on

[ 0 , 1 ]

displaystyle [0,1]

, preceded by 0 and succeeded by 1. The distributions of a parameter inferred from considering many such datasets

D

J

displaystyle mathcal D ^ J

are then interpretable as posterior distributions on that parameter.[20] Smooth bootstrap[edit] Under this scheme, a small amount of (usually normally distributed) zero-centered random noise is added onto each resampled observation. This is equivalent to sampling from a kernel density estimate of the data. Parametric bootstrap[edit] In this case a parametric model is fitted to the data, often by maximum likelihood, and samples of random numbers are drawn from this fitted model. Usually the sample drawn has the same sample size as the original data. Then the quantity, or estimate, of interest is calculated from these data. This sampling process is repeated many times as for other bootstrap methods. The use of a parametric model at the sampling stage of the bootstrap methodology leads to procedures which are different from those obtained by applying basic statistical theory to inference for the same model. Resampling residuals[edit] Another approach to bootstrapping in regression problems is to resample residuals. The method proceeds as follows.

Fit the model and retain the fitted values

y ^

i

displaystyle hat y _ i

and the residuals

ϵ ^

i

=

y

i

−

y ^

i

, ( i = 1 , … , n )

displaystyle hat epsilon _ i =y_ i - hat y _ i ,(i=1,dots ,n)

. For each pair, (xi, yi), in which xi is the (possibly multivariate) explanatory variable, add a randomly resampled residual,

ϵ ^

j

displaystyle hat epsilon _ j

, to the response variable yi. In other words, create synthetic response variables

y

i

∗

=

y

i

+

ϵ ^

j

displaystyle y_ i ^ * =y_ i + hat epsilon _ j

where j is selected randomly from the list (1, …, n) for every i. Refit the model using the fictitious response variables

y

i

∗

displaystyle y_ i ^ *

, and retain the quantities of interest (often the parameters,

μ ^

i

∗

displaystyle hat mu _ i ^ *

, estimated from the synthetic

y

i

∗

displaystyle y_ i ^ *

). Repeat steps 2 and 3 a large number of times.

This scheme has the advantage that it retains the information in the
explanatory variables. However, a question arises as to which
residuals to resample. Raw residuals are one option; another is
studentized residuals (in linear regression). Whilst there are
arguments in favour of using studentized residuals; in practice, it
often makes little difference and it is easy to run both schemes and
compare the results against each other.
Gaussian process regression bootstrap[edit]
When data are temporally correlated, straightforward bootstrapping
destroys the inherent correlations. This method uses Gaussian process
regression to fit a probabilistic model from which replicates may then
be drawn.
**Gaussian processes**

Gaussian processes are methods from Bayesian non-parametric
statistics but are here used to construct a parametric bootstrap
approach, which implicitly allows the time-dependence of the data to
be taken into account.
Wild bootstrap[edit]
The Wild bootstrap, proposed originally by Wu (1986),[21] is suited
when the model exhibits heteroskedasticity. The idea is, like the
residual bootstrap, to leave the regressors at their sample value, but
to resample the response variable based on the residuals values. That
is, for each replicate, one computes a new

y

displaystyle y

based on

y

i

∗

=

y ^

i

+

ϵ ^

i

v

i

displaystyle y_ i ^ * = hat y _ i + hat epsilon _ i v_ i

so the residuals are randomly multiplied by a random variable

v

i

displaystyle v_ i

with mean 0 and variance 1. This method assumes that the 'true' residual distribution is symmetric and can offer advantages over simple residual sampling for smaller sample sizes. Different forms are used for the random variable

v

i

displaystyle v_ i

, such as

The standard normal distribution

A distribution suggested by Mammen (1993).[22]

v

i

=

− (

5

− 1 )

/

2

with prob.

(

5

+ 1 )

/

( 2

5

)

(

5

+ 1 )

/

2

with prob.

(

5

− 1 )

/

( 2

5

)

displaystyle v_ i =left begin matrix -( sqrt 5 -1)/2& mbox with prob. ( sqrt 5 +1)/(2 sqrt 5 )\( sqrt 5 +1)/2& mbox with prob. ( sqrt 5 -1)/(2 sqrt 5 )end matrix right.

.

Approximately, Mammen's distribution is:

v

i

=

− 0.6180

with prob.

0.7286

1.6180

with prob.

0.2764

displaystyle v_ i =left begin matrix -0.6180& mbox with prob. 0.7286\1.6180& mbox with prob. 0.2764end matrix right.

Or the simpler distribution, linked to the Rademacher distribution:

v

i

=

− 1

with prob.

1

/

2

1

with prob.

1

/

2

displaystyle v_ i =left begin matrix -1& mbox with prob. 1/2\1& mbox with prob. 1/2end matrix right.

Block bootstrap[edit]
The block bootstrap is used when the data, or the errors in a model,
are correlated. In this case, a simple case or residual resampling
will fail, as it is not able to replicate the correlation in the data.
The block bootstrap tries to replicate the correlation by resampling
instead blocks of data. The block bootstrap has been used mainly with
data correlated in time (i.e. time series) but can also be used with
data correlated in space, or among groups (so-called cluster data).
Time series: Simple block bootstrap[edit]
In the (simple) block bootstrap, the variable of interest is split
into non-overlapping blocks.
Time series: Moving block bootstrap[edit]
In the moving block bootstrap, introduced by Künsch (1989),[23] data
is split into n-b+1 overlapping blocks of length b: Observation 1 to b
will be block 1, observation 2 to b+1 will be block 2 etc. Then from
these n-b+1 blocks, n/b blocks will be drawn at random with
replacement. Then aligning these n/b blocks in the order they were
picked, will give the bootstrap observations.
This bootstrap works with dependent data, however, the bootstrapped
observations will not be stationary anymore by construction. But, it
was shown that varying randomly the block length can avoid this
problem.[24] This method is known as the stationary bootstrap. Other
related modifications of the moving block bootstrap are the Markovian
bootstrap and a stationary bootstrap method that matches subsequent
blocks based on standard deviation matching.
Cluster data: block bootstrap[edit]
Cluster data describes data where many observations per unit are
observed. This could be observing many firms in many states, or
observing students in many classes. In such cases, the correlation
structure is simplified, and one does usually make the assumption that
data is correlated with a group/cluster, but independent between
groups/clusters. The structure of the block bootstrap is easily
obtained (where the block just corresponds to the group), and usually
only the groups are resampled, while the observations within the
groups are left unchanged. Cameron et al. (2008) [25] discusses this
for clustered errors in linear regression.
Choice of statistic[edit]
The bootstrap distribution of a point estimator of a population
parameter has been used to produce a bootstrapped confidence interval
for the parameter's true value, if the parameter can be written as a
function of the population's distribution.
Population parameters are estimated with many point estimators.
Popular families of point-estimators include mean-unbiased
minimum-variance estimators, median-unbiased estimators, Bayesian
estimators (for example, the posterior distribution's mode, median,
mean), and maximum-likelihood estimators.
A Bayesian point estimator and a maximum-likelihood estimator have
good performance when the sample size is infinite, according to
asymptotic theory. For practical problems with finite samples, other
estimators may be preferable.
**Asymptotic theory** suggests techniques
that often improve the performance of bootstrapped estimators; the
bootstrapping of a maximum-likelihood estimator may often be improved
using transformations related to pivotal quantities.[26]
Deriving confidence intervals from the bootstrap distribution[edit]
The bootstrap distribution of a parameter-estimator has been used to
calculate confidence intervals for its population-parameter.[citation
needed]
Bias, asymmetry, and confidence intervals[edit]

Bias: The bootstrap distribution and the sample may disagree systematically, in which case bias may occur.

If the bootstrap distribution of an estimator is symmetric, then percentile confidence-interval are often used; such intervals are appropriate especially for median-unbiased estimators of minimum risk (with respect to an absolute loss function). Bias in the bootstrap distribution will lead to bias in the confidence-interval. Otherwise, if the bootstrap distribution is non-symmetric, then percentile confidence-intervals are often inappropriate.

Methods for bootstrap confidence intervals[edit] There are several methods for constructing confidence intervals from the bootstrap distribution of a real parameter:

Basic Bootstrap. The basic bootstrap is the simplest scheme to construct the confidence interval: one simply takes the empirical quantiles from the bootstrap distribution of the parameter (see Davison and Hinkley 1997, equ. 5.6 p. 194):

( 2 θ −

θ

( 1 − α

/

2 )

∗

; 2 θ −

θ

( α

/

2 )

∗

)

displaystyle (2theta -theta _ (1-alpha /2) ^ * ;2theta -theta _ (alpha /2) ^ * )

where

θ

( 1 − α

/

2 )

∗

displaystyle theta _ (1-alpha /2) ^ *

denotes the

1 − α

/

2

displaystyle 1-alpha /2

percentile of the bootstrapped coefficients

θ

∗

displaystyle theta ^ *

.

**Percentile**

Percentile Bootstrap. The percentile bootstrap proceeds in a similar
way to the basic bootstrap, using percentiles of the bootstrap
distribution, but with a different formula (note the inversion of the
left and right quantiles!):

(

θ

( α

/

2 )

∗

;

θ

( 1 − α

/

2 )

∗

)

displaystyle (theta _ (alpha /2) ^ * ;theta _ (1-alpha /2) ^ * )

where

θ

( 1 − α

/

2 )

∗

displaystyle theta _ (1-alpha /2) ^ *

denotes the

1 − α

/

2

displaystyle 1-alpha /2

percentile of the bootstrapped coefficients

θ

∗

displaystyle theta ^ *

.

See Davison and Hinkley (1997, equ. 5.18 p. 203) and Efron and Tibshirani (1993, equ 13.5 p. 171). This method can be applied to any statistic. It will work well in cases where the bootstrap distribution is symmetrical and centered on the observed statistic[27] and where the sample statistic is median-unbiased and has maximum concentration (or minimum risk with respect to an absolute value loss function). In other cases, the percentile bootstrap can be too narrow.[citation needed] When working with small sample sizes (i.e., less than 50), the percentile confidence intervals for (for example) the variance statistic will be too narrow. So that with a sample of 20 points, 90% confidence interval will include the true variance only 78% of the time[28]

Studentized Bootstrap. The studentized bootstrap, also called
bootstrap-t, works similarly as the usual confidence interval, but
replaces the quantiles from the normal or student approximation by the
quantiles from the bootstrap distribution of the
**Student's t-test**

Student's t-test (see
Davison and Hinkley 1997, equ. 5.7 p. 194 and Efron and
Tibshirani 1993 equ 12.22, p. 160):

( θ −

t

( 1 − α

/

2 )

∗

⋅

s e

^

θ

; θ −

t

( α

/

2 )

∗

⋅

s e

^

θ

)

displaystyle (theta -t_ (1-alpha /2) ^ * cdot hat se _ theta ;theta -t_ (alpha /2) ^ * cdot hat se _ theta )

where

t

( 1 − α

/

2 )

∗

displaystyle t_ (1-alpha /2) ^ *

denotes the

1 − α

/

2

displaystyle 1-alpha /2

percentile of the bootstrapped
**Student's t-test**

Student's t-test

t

∗

= (

θ ^

∗

−

θ ^

)

/

s e

^

θ ^

∗

displaystyle t^ * =( hat theta ^ * - hat theta )/ hat se _ hat theta ^ *

, while

s e

^

θ

displaystyle hat se _ theta

is the estimated standard error of the coefficient in the original model.

The studentized test enjoys optimal properties as the statistic that is bootstrapped is pivotal (i.e. it does not depend on nuisance parameters as the t-test follows asymptotically a N(0,1) distribution), unlike the percentile bootstrap.

Bias-Corrected Bootstrap - adjusts for bias in the bootstrap distribution. Accelerated Bootstrap - The bias-corrected and accelerated (BCa) bootstrap, by Efron (1987),[14] adjusts for both bias and skewness in the bootstrap distribution. This approach is accurate in a wide variety of settings, has reasonable computation requirements, and produces reasonably narrow intervals.[citation needed]

Example applications[edit]

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Smoothed bootstrap[edit]
In 1878,
**Simon Newcomb**

Simon Newcomb took observations on the speed of light.[29]
The data set contains two outliers, which greatly influence the sample
mean. (Note that the sample mean need not be a consistent estimator
for any population mean, because no mean need exist for a heavy-tailed
distribution.) A well-defined and robust statistic for central
tendency is the sample median, which is consistent and median-unbiased
for the population median.
The bootstrap distribution for Newcomb's data appears below. A
convolution method of regularization reduces the discreteness of the
bootstrap distribution by adding a small amount of N(0, σ2) random
noise to each bootstrap sample. A conventional choice is

σ = 1

/

n

displaystyle sigma =1/ sqrt n

for sample size n.[citation needed] Histograms of the bootstrap distribution and the smooth bootstrap distribution appear below. The bootstrap distribution of the sample-median has only a small number of values. The smoothed bootstrap distribution has a richer support.

In this example, the bootstrapped 95% (percentile) confidence-interval for the population median is (26, 28.5), which is close to the interval for (25.98, 28.46) for the smoothed bootstrap. Relation to other approaches to inference[edit] Relationship to other resampling methods[edit] The bootstrap is distinguished from:

the jackknife procedure, used to estimate biases of sample statistics and to estimate variances, and cross-validation, in which the parameters (e.g., regression weights, factor loadings) that are estimated in one subsample are applied to another subsample.

For more details see bootstrap resampling.
**Bootstrap aggregating**

Bootstrap aggregating (bagging) is a meta-algorithm based on averaging
the results of multiple bootstrap samples.
U-statistics[edit]
Main article: U-statistic
In situations where an obvious statistic can be devised to measure a
required characteristic using only a small number, r, of data items, a
corresponding statistic based on the entire sample can be formulated.
Given an r-sample statistic, one can create an n-sample statistic by
something similar to bootstrapping (taking the average of the
statistic over all subsamples of size r). This procedure is known to
have certain good properties and the result is a U-statistic. The
sample mean and sample variance are of this form, for r=1 and r=2.
See also[edit]

Accuracy and precision Bootstrap aggregating Empirical likelihood Imputation (statistics) Reliability (statistics) Reproducibility Resampling

References[edit]

^ Efron, B.; Tibshirani, R. (1993). An Introduction to the Bootstrap.
Boca Raton, FL: Chapman & Hall/CRC. ISBN 0-412-04231-2.
software
^ Second Thoughts on the Bootstrap - Bradley Efron, 2003
^ Varian, H.(2005). "Bootstrap Tutorial". Mathematica Journal, 9,
768-775.
^ Weisstein, Eric W. "Bootstrap Methods." From MathWorld--A Wolfram
Web Resource. http://mathworld.wolfram.com/BootstrapMethods.html
^ Notes for Earliest Known Uses of Some of the Words of Mathematics:
Bootstrap (John Aldrich)
^ Earliest Known Uses of Some of the Words of Mathematics (B) (Jeff
Miller)
^ Efron, B. (1979). "Bootstrap methods: Another look at the
jackknife". The Annals of Statistics. 7 (1): 1–26.
doi:10.1214/aos/1176344552.
^ Quenouille M (1949) Approximate tests of correlation in time-series.
J Roy Statist Soc Ser B 11 68–84
^ Tukey J (1958) Bias and confidence in not-quite large samples
(abstract). Ann Math Statist 29 614
^ Jaeckel L (1972) The infinitesimal jackknife. Memorandum
MM72-1215-11, Bell Lab
^ Bickel P, Freeman D (1981) Some asymptotic theory for the bootstrap.
Ann Statist 9 1196–1217
^ Singh K (1981) On the asymptotic accuracy of Efron’s bootstrap.
Ann Statist 9 1187–1195
^ Rubin D (1981). The Bayesian bootstrap. Ann Statist 9 130–134
^ a b Efron, B. (1987). "Better Bootstrap Confidence Intervals".
Journal of the American Statistical Association. Journal of the
American Statistical Association, Vol. 82, No. 397. 82 (397):
171–185. doi:10.2307/2289144. JSTOR 2289144.
^ Diciccio T, Efron B (1992) More accurate confidence intervals in
exponential families.
**Biometrika**

Biometrika 79 231–245
^ DiCiccio TJ, Efron B (1996) Bootstrap confidence intervals (with
Discussion). Statistical Science 11: 189-228
^ Adèr, H. J., Mellenbergh G. J., & Hand, D. J. (2008). Advising
on research methods: A consultant's companion. Huizen, The
Netherlands: Johannes van Kessel Publishing.
ISBN 978-90-79418-01-5
^ Bootstrap of the mean in the infinite variance case Athreya, K.B.
Ann Stats vol 15 (2) 1987 724-731
^ Efron B., R. J. Tibshirani, An introduction to the bootstrap,
Chapman & Hall/CRC 1998
^ Rubin, D. B. (1981). "The Bayesian bootstrap". Annals of Statistics,
9, 130.
^ Wu, C.F.J. (1986). "Jackknife, bootstrap and other resampling
methods in regression analysis (with discussions)". Annals of
Statistics. 14: 1261–1350. doi:10.1214/aos/1176350142.
^ Mammen, E. (Mar 1993). "Bootstrap and wild bootstrap for high
dimensional linear models". Annals of Statistics. 21 (1): 255–285.
doi:10.1214/aos/1176349025.
^ Künsch, H. R. (1989). “The jackknife and the bootstrap for
general stationary observations,” Annals of Statistics, 17,
1217–1241.
^ Politis, D.N. and Romano, J.P. (1994). The stationary bootstrap.
Journal of the American Statistical Association, 89, 1303-1313.
^ Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008):
“Bootstrap-based im- provements for inference with clustered
errors,” Review of Economics and Statistics, 90, 414–427
^ Davison, A. C.; Hinkley, D. V. (1997). Bootstrap methods and their
application. Cambridge Series in Statistical and Probabilistic
Mathematics. Cambridge University Press. ISBN 0-521-57391-2.
software.
^ Efron, B. (1982). The jackknife, the bootstrap, and other resampling
plans. 38. Society of Industrial and Applied Mathematics CBMS-NSF
Monographs. ISBN 0-89871-179-7.
^ Scheiner, S. (1998). Design and Analysis of Ecological Experiments.
CRC Press. ISBN 0412035618.
^ Data from examples in Bayesian Data Analysis

Further reading[edit]

Diaconis, P.; Efron, B. (May 1983). "Computer-intensive methods in statistics" (PDF). Scientific American: 116–130. popular-science Efron, B. (1981). "Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods". Biometrika. 68 (3): 589–599. doi:10.1093/biomet/68.3.589. Hesterberg, T. C.; D. S. Moore; S. Monaghan; A. Clipson & R. Epstein (2005). "Bootstrap methods and permutation tests". In David S. Moore & George McCabe. Introduction to the Practice of Statistics (pdf). software.

External links[edit]

Bootstrap sampling tutorial using MS Excel Bootstrap example to simulate stock prices using MS Excel bootstrapping tutorial package animation

Software[edit]

Statistics101: Resampling, Bootstrap, Monte Carlo Simulation program. Free program written in Java to run on any operating system.

v t e

Statistics

Outline Index

Descriptive statistics

Continuous data

Center

Mean

arithmetic geometric harmonic

Median Mode

Dispersion

Variance Standard deviation Coefficient of variation Percentile Range Interquartile range

Shape

Central limit theorem Moments

Skewness Kurtosis L-moments

Count data

Index of dispersion

Summary tables

Grouped data Frequency distribution Contingency table

Dependence

Pearson product-moment correlation Rank correlation

Spearman's rho Kendall's tau

Partial correlation Scatter plot

Graphics

Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q–Q plot Run chart Scatter plot Stem-and-leaf display Radar chart

Data collection

Study design

Population
Statistic
Effect size
Statistical power
**Sample size** determination
Missing data

Survey methodology

Sampling

stratified cluster

Standard error Opinion poll Questionnaire

Controlled experiments

Design

control optimal

Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment

Uncontrolled studies

Observational study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Population Statistic Probability distribution Sampling distribution

Order statistic

Empirical distribution

Density estimation

Statistical model

Lp space

Parameter

location scale shape

Parametric family

Likelihood (monotone) Location–scale family Exponential family

Completeness Sufficiency Statistical functional

Bootstrap U V

Optimal decision

loss function

Efficiency Statistical distance

divergence

Asymptotics Robustness

Frequentist inference

Point estimation

Estimating equations

Maximum likelihood Method of moments M-estimator Minimum distance

Unbiased estimators

Mean-unbiased minimum-variance

Rao–Blackwellization Lehmann–Scheffé theorem

**Median**

Median unbiased

Plug-in

Interval estimation

Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling

Bootstrap Jackknife

Testing hypotheses

1- & 2-tails Power

Uniformly most powerful test

Permutation test

Randomization test

Multiple comparisons

Parametric tests

Likelihood-ratio Wald Score

Specific tests

**Z-test** (normal)
Student's t-test
F-test

Goodness of fit

Chi-squared G-test Kolmogorov–Smirnov Anderson–Darling Lilliefors Jarque–Bera Normality (Shapiro–Wilk) Likelihood-ratio test Model selection

Cross validation AIC BIC

Rank statistics

Sign

Sample median

Signed rank (Wilcoxon)

Hodges–Lehmann estimator

Rank sum (Mann–Whitney) Nonparametric anova

1-way (Kruskal–Wallis) 2-way (Friedman) Ordered alternative (Jonckheere–Terpstra)

Bayesian inference

Bayesian probability

prior posterior

Credible interval Bayes factor Bayesian estimator

Maximum posterior estimator

Correlation Regression analysis

Correlation

Pearson product-moment
Partial correlation
**Confounding**

Confounding variable
Coefficient of determination

Regression analysis

Errors and residuals
Regression model validation
Mixed effects models
Simultaneous equations models
**Multivariate adaptive regression splines** (MARS)

Linear regression

Simple linear regression Ordinary least squares General linear model Bayesian regression

Non-standard predictors

Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity

Generalized linear model

Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance

**Analysis of variance**

Analysis of variance (ANOVA, anova)
Analysis of covariance
Multivariate ANOVA
Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

Categorical

Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test

Multivariate

Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model

Factor analysis

Multivariate distributions

Elliptical distributions

Normal

Time-series

General

Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality

Specific tests

Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey

Time domain

**Autocorrelation**

Autocorrelation (ACF)

partial (PACF)

**Cross-correlation**

Cross-correlation (XCF)
ARMA model
ARIMA model (Box–Jenkins)
**Autoregressive conditional heteroskedasticity** (ARCH)
**Vector autoregression** (VAR)

Frequency domain

Spectral density estimation Fourier analysis Wavelet Whittle likelihood

Survival

Survival function

**Kaplan–Meier estimator**

Kaplan–Meier estimator (product limit)
Proportional hazards models
Accelerated failure time (AFT) model
First hitting time

Hazard function

Nelson–Aalen estimator

Test

Log-rank test

Applications

Biostatistics

Bioinformatics Clinical trials / studies Epidemiology Medical statistics

Engineering statistics

Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification

Social statistics

Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population statistics Psychometrics

Spatial statistics

Cartography Environmental statistics Geographic information system Geostatistics Kriging

Category Portal Commons