Lecture 9—Wednesday, Sept. 17, 2003
What was covered?
 Overview and review of estimation.
 Introduction to the bootstrap
 Bootstrap confidence intervals
Overview of Estimation
In class and/or in the textbook, we've dealt with four types
of estimation methods
 Method of moments (also called plugin) estimators.
 These are easy to calculate but typically have no theory
to back them up or to determine their precision, unless they happen to be
estimators of another class also.
 Estimators based on nonstatistical theory.
 In this category I would put any estimator developed from
biological criteria rather than statistical ones. Many of the diversity
estimators and the like are examples. If there is any statistical theory
(such as formula for standard errors) associated with them it will be on
a case by case basis.
 Maximum likelihood estimators (mles)
 There is a full statistical theory associated with them.
 Their main drawback is that they're not always relevant
in specific cases. In particular, to construct an mle you first need a probability
model for your data. It may not always be obvious what the appropriate probability
model might be and in many cases there will be competing candidates.
 Least squares estimators.
 We have not discussed these, but your text does. They
can always be calculated but they may not always be appropriate.
 Least squares estimation yields the value
that minimizes
with respect to
for a set of data values
and a given function f.
 If you can assume normality, then standard errors of
the estimators can be constructed and hypothesis tests conducted. In linear
models where normality can be assumed, least squares estimators are optimal.
Outside of linear models and normally distributed response variables,
least squares is generally inappropriate.
 The popularity of least squares estimators is largely
a historical accident and owes primarily to the fact that explicit analytical
solutions can be obtained using calculus.
 Any situation where least squares estimation is appropriate
can also be formulated as a maximum likelihood estimation problem in which
a normal probability model is selected.
Note: the sample mean is a method of moments estimator, a maximum
likelihood estimator (under virtually all probability models), and a least squares
estimator. In a least squares sense, it is the value of c that minimizes
the expression .
Bootstrapping

Bootstrapping is a computerintensive technique that can
be used to obtain standard errors for estimators regardless of their origin.
It can also be used to construct interval estimates of parameters.

Bootstrapping is especially useful in situations where
there is no statistical theory to guide us or where standard statistical
theory is inappropriate or suspect. Thus it is especially appropriate for
categories 1, 2 , and even 4 in the above overview of estimation theory.
 The bootstrap was invented by Bradley Efron in the middle
1970s. The name was chosen because in the method one seems to use the data
to pull oneself up by one's own bootstraps. Efron remarked in an early paper
that he had contemplated calling the method the "shotgun" because
it "... can blow the head off any problem if the statistician can stand
the resulting mess."
 There are two distinct flavors of the bootstrap: the parametric
bootstrap and the nonparametric bootstrap.
 In the parametric bootstrap a probability model is assumed
for the underlying population.
 In the nonparametric bootstrap we proceed without a
probability model.
 When most statisticians use the word "bootstrap"
they are referring to the nonparametric bootstrap.
Parametric Bootstrap
 Suppose we're studying seed shadows so we measure the distance
from a source plant to a random selection of propagules. Below is a random
sample of 10 such distances.
41.53, 18.73, 2.99, 30.34, 12.33, 117.52, 73.02, 223.63, 4.00, 26.78
 We assume this is a random sample from an exponential distribution
with parameter ,
a common probability model for seed shadows. We wish to estimate
and obtain some measure of the precision of our estimate.
 Using the method of moments, maximum likelihood theory,
divination, or whatever, we decide to estimate
by .
For our data this yields
= 0.018153.
 We next use a statistical computer package, (R, or whatever),
to generate 10 realizations from an exponential distribution with
= 0.018153. (In R this could be done with rexp(10,0.018153).
The resulting sample is called the first bootstrap sample. Using our estimation
formula above, we then estimate
from this sample. Denote this estimate by .
 Next we repeat this step B times. (B is often
taken to be 1000 or more.) Obtain bootstrap estimates .
 Calculate and
use this to obtain the standard error of
as follows.
Nonparametric Bootstrap
 Consider again the seed shadow example above. This time
don't assume any probability model at all but suppose there is still a parameter
of interest
to us.
 Estimate
in whatever fashion seems reasonable. For simplicity, assume we use the same
estimator as above.
 Construct the empirical distribution function of our sample.
For a sample of size n, the empirical density function assigns a weight
of
to each data value.
 The empirical distribution function is then given by .
This is a step function that jumps a height
(or some multiple of
if there are repeated observations) at each unique data value. The empirical
distribution function is an estimator of the population cumulative distribution
function.
 Using the empirical density function as the population model,
take a sample of size 10 from this "population". In the example
above we would sample from the ten data values assigning equal probability
to each.
 Notice that using this protocol we may select the same
observation more than once in our sample. This is called sampling with
replacement.
 In R (or any other programming language for that matter)
we could carry out this procedure by first assigning the data values the
numbers 1 through 10. We then select observations based on the return
value of
ceiling(10*runif(10)
, where runif
is
the R function that returns a random number chosen uniformly from the
interval [0, 1] and ceiling
is the ceiling function (the
function that rounds its argument up to the nearest integer).
 A simpler way in R is to use the sample function
as will be demonstrated this week in lab.
 Having obtained the bootstrap sample calculate
for the sample (just as was done in the parametric bootstrap). The rest of
the procedure follows that outlined for the parametric bootstrap except that
we use the empirical distribution to generate our samples.
 Why does this make sense?
 Although it seems like there's no probability model
guiding the selection of our sample, in reality there is one. It's the
empirical probability modelthe probability model suggested by our data.
In this model each data point is assigned a probability based on its frequency.
 The empirical probability function is a best estimate
of the underyling population probability distribution.
Bootstrap Confidence Intervals
There are five basic bootstrap confidence intervals implemented
in the boot library of R. I describe each in turn.
Normal (Standard) Bootstrap Confidence Interval:
type='norm'
 A generic template for a confidence interval is the following.

is the estimate of
from our sample. For
we use a bootstrap estimate, the standard deviation of the bootstrap estimates
of .
 To justify the use of this confidence interval we still
must meet the usual assumptionslarge sample size or normal population, etc.
Percentile Bootstrap Confidence Interval:
type='perc'
 Rather than appeal to a normal or tdistribution
to obtain the quantiles for a confidence interval, we use the bootstrap distribution
itself.
 To obtain a 95% confidence interval, e.g., we generate the
bootstrap distribution and determine the 2.5 percentile and the 97.5 percentile
of the bootstrap. These numbers form the boundaries of our confidence interval.
 Percentile bootstrap confidence intervals tend to be highly
asymmetric.
Basic Bootstrap Confidence Interval:
type='basic' (not covered in lecture)
 The algebra is a bit confusing for this one but in many
respects it resembles the percentile confidence interval. The basic idea behind
this one is that the bootstrap distribution of bootstrap values about the
sample estimate should resemble the sampling distribution of the sample estimate
about the true population parameter. Symbolically, the distribution of ,
which we can observe, should resemble the distribution of ,
which we can't (unless we take multiple samples).
 Put another way, we want to find L and U so
that
but we use
to actually find them and hope that they're close to the
ones we actually want.
 Rewriting the second probability statement we have
 Next we use the bootstrap distribution to find the quantiles
we need. Call them
and .
Setting them equal to the endpoints of the interval defined in the above probability
statement and solving for L and U yields
 Next return to the original probability statement, the one
we actually care about, and solve the inequality for .
 Lastly, plug in the values for L and U obtained
from the bootstrap distribution.
 These are the limits that are returned by R for the basic
bootstrap confidence interval.
Studentizedt (Percentilet) Bootstrap Confidence
Interval: type='stud'
 The studentizedt bootstrap confidence interval takes
the same form as the normal confidence interval except that instead of using
the quantiles from a tdistribution (or a normal distribution) a bootstrapped
tdistribution is constructed from which the quantiles are computed.
 For each bootstrap sample the following quantity is calculated
 The complication with this formula is that we need some
way of estimating ,
the standard error of the bootstrap estimate in each bootstrap sample. In
R we actually need to enter a formula for this quantity. So if
is a statistic for which there exists a variance formula then we can proceed.
Note: in principle it would be possible to bootstrap the bootstrap sample
to get an estimate of the standard error but this is very resource intensive.
So this method works best when a formula for the variance already exists.
 Having obtained the B values of
obtain the quantiles needed for the confidence interval. To generate a 95%
confidence interval, e.g., obtain the 2.5 percentile and the 97.5 percentile
of the bootstrap t distribution. Use these numbers like ordinary tstatistics
and construct the confidence interval in the usual way.
where
is the bootstrap estimate of the standard error of .
Notice that the quantiles of the bootstrapped t are used in what is
perhaps the reversed order from what you might expect.
Biascorrected and Accelerated Bootstrap
Confidence Interval: type='bca'
 The biascorrected and accelerated bootstrap confidence
interval attempts to shift and scale the percentile bootstrap confidence interval
to compensate for bias. The formulas for doing this are fairly complicated
and rather unintuitive.
 There are two parameters involved, one called
attempts to correct the bias. A second, ,
is called the acceleration parameter.
 There is no unanimity of opinion on whether BCa yields the
best bootstrap confidence intervals. In my limited experience I have found
that there often problems in trying to calculate these intervals so that warnings
are printed suggesting that the calculated estimates may be suspect.
 There is an approximation to the BCa method, called the
ABC method, for approximate bootstrap confidence interval,
that finds the endpoints of the BCa method analytically. I've seen cases where
the ABC method seems to work when the BCa method does not or where the BCa
method runs out of memory. In the boot library the function abc.ci
finds ABC endpoints. See the lab 4 notes
for more information.
Vocabulary
 Least squares estimate
 Bootstrap
 Parametric bootstrap
 Nonparametric bootstrap
 Biascorrected and accelerated bootstrap
 Normal (standard) bootstrap confidence interval
 Basic bootstrap confidence interval
 Percentile bootstrap confidence interval
 Bootstrapt confidence interval
 Biascorrected and accelerated bootstrap confidence interval
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