STAT 572: Bootstrap Project

Group Members: Cindy Bothwell Erik Barry Erhardt Nina GreenbergCasey Richardson Zachary Taylor

Histograms of Complex Population Distribution

Histograms of Population Sampling Distribution of the Median and Estimated

Bootstrap Sampling Distributions

What is a Bootstrap

A method of Resampling: creating many samples from a single sample

Generally, resampling is done with replacement

Used to develop a sampling distribution of statistics such as mean, median, proportion, others.

The Bootstrap and Complex Surveys

Number of bootstrap samples– n = sample size, N = population size– Possible resamples nn (example n=200, 200200=1.6x10460)

Too many possibilities N!/[n!(N-n)!], limit to B a large number, (example = 1000) - the Monte Carlo approximation

Determine sampling distribution with parameters Calculate variance in the normal way

Advantages and Disadvantages

Advantages:– Avoids the costs of taking new samples (Estimate a

sampling distribution when only one sample is available)– Checking parametric assumptions– Used when parametric assumptions cannot be made or are

very complicated– Estimation of variance in quantiles

Disadvantages:– Relies on a representative sample– Variability due to finite replications (Monte Carlo)

Computations

With more computing power available, bootstrap is possible for a large number of resamples

Possible programs:– Matlab– Minitab– SAS– Excel– S-Plus– SPSS– Fathom

Bootstrap using SURVEY program

Main parameter of interest is the median price that all households in Lockhart City are wiling to pay for cable.

The price that a household is willing to pay for cable is positively correlated with average-district house value.

Districts in Lockhart City are divided into strata based on average house value.

Estimate the variance and create 95% CI

Lockhart City Strata Characteristics:

Take a stratified random sample of size 200 using proportional allocation.

Using the stratified random sample, implement the general bootstrap procedure, BWO, and mirror-match.

Variations of the Bootstrap in Strata

General Bootstrap– Mimic the original sampling method

BWO: Bootstrap Without Replacement– Grow the sample to the size of the population

Mirror-Match– Repeated miniature resamples

BWO: Bootstrap Without Replacement

Grow the sample to the size of the population For each stratum L, create a pseudo-

population by replicating the sample kL times.

Resample n’L units from each stratum without replacement to obtain a single bootstrap sample for stratum L.

Repeat a large number of times

BWO: Variable Definitions

LLL fnn 1' where L

LL N

nf = stratum sampling fraction

L

L

L

LL n

f

n

Nk

11 where Ln' and Lk are integers

Disadvantages of extended BWO

NL must be known

n’L and kL are often non-integers

Must bracket between integers if n’L and kL are non-integer

Computing time

Mirror-Match

Repeated miniature resamples Resample size is determined to match the proportion

of the original sample size to the population sample size (nL/NL).

Using the resample size n’L, we resample n’L units (SRSWOR) from each stratum L.

Repeat previous step kL times with replacement to obtain a single bootstrap sample for stratum L.

Repeat a large number times

Mirror-Match: Variable Definitions

L

LL N

nn

2

' LL

LLL fn

fnk

1'

*1

where: L

LL n

nf

'* = stratum resample fraction

L

LL N

nf = original stratum sample fraction

Mirror Match: Disadvantages

NL must be known

kL is often non-integer

Must bracket between integers when kL is non-integer

Computing time

Estimation of the Population Sampling Distributions

100,000 independent stratified random samples.

Medians computed and plotted to form empirical sampling distributions.

Variables: house value, cable price, and TV hours.

Estimation of the Population Sampling Distributions

Simulations

Matlab code: General, BWO, and Mirror-match. Two independent stratified random samples from

Lockhart City. Comparison of the sample bootstrap sampling

distributions with the population sampling distributions.

95% confidence intervals were determined bootstrap 2.5 and 97.5 percentiles.

Sampling Distributions 1

Sampling Distributions 2

Confidence Intervals

Variable Population Estimate

Empirical CI Standard Bootstrap

BWO Mirror-Match

House (1) 74740 (72027,75954) (73092,75616) (73119,75600) (73119,75733) Price (1) 10 (10,10) (10,10) (10,10) (10,10) Hours (1) 40 (28.5,41) (32.5,47.0) (32.5,47.0) (32.0,47.0) House (2) 74740 (72027,75954) (72079,75155) (71995,75155) (72010,75155) Price (2) 10 (10,10) (10,10) (10,10) (10,10) Hours (2) 40 (28.5,41) (30.5,39.5) (29.5,39.5) (30.5,40.0)

The Empirical verses the Bootstrap Sampling Distributions

Bootstrap sampling distributions are expected to mimic actual sampling distributions.

Bootstrap sampling is sensitive to individual samples.

The shape of bootstrap sampling distributions may vary, but the statistic of interest and its variance are considered accurate.

Comparison of Bootstrap Methods

Empirical Coverages

The empirical coverages were close to the expected 95%. They differed very little between the different bootstrap procedures.

House Value Cable Price TV Hours General .936 1 .957 BWO .933 1 .959 Mirror-Match .94 1 .961

Empirical Coverages

Empirical coverages are dependent on the type of confidence interval that was originally selected.

Our confidence intervals were calculated from the 2.5 and 97.5 percentiles of each bootstrap distribution.

There are many different types of bootstrap confidence intervals. The one we selected, although intuitive in design, is considered generally biased (Bedrick 2006).

Computer Processing Times

Computer processing times varied greatly. Mean processing time per sample in seconds.

House Value Cable Price TV Hours General .11961 .11502 .12112 BWO 45.765 45.769 45.812 Mirror-Match 35.18 35.164 35.169

Computer Processing Times

BWO took 381 times as long as general bootstrapping procedures.

Mirror-match took 293 times as long as general bootstrapping procedures.

For our study, the BWO and mirror-match conferred no advantage over general bootstrapping with regard to statistical estimates. However, their vastly greater processing times are a great disadvantage.

CONCLUSIONS: General Bootstrap verses BWO and Mirror-Match

BWO and Mirror-match procedures are designed to mimic complex sampling designs.

We only analyzed stratified samples of 200 from a fictitious city.

BWO and Mirror-match methods may be advantageous in other complex sampling scenarios.