STATISTICAL COMPUTING LAB

SCL:
3381 Rood Hall
phone: (269) 3872852
slab@stat.wmich.edu
Directors:
Dr. Joseph W. McKean
5506 Everett Tower
Department of Statistics
phone: (269) 3874541
joe@stat.wmich.edu
Dr. John Kapenga
Department of Computer Science
phone: (269) 3875657
john@cs.wmich.edu
Coodinator:
John Kloke
6610 Everett Tower
Department of Statistics
john.kloke@wmich.edu

This software is designed to run the double bootstrap procedure described
in the article,
A Double Bootstrap Method to Analyze an Intervention Time Series Model with
Autoregressive Error Terms, (2000), Psychological Methods, 5, 87101
,S. McKnight, J.W. McKean and B. Huitema.
This procedure is intended for use on a model of the form:
Y = X*b + Error,
where the errors follow an autoregressive time series of order p.
The double bootstrap procedure is a two stage procedure.

The first stage uses a bootstrap approach to
obtain bias adjusted estimates of the autoregressive parameters. These estimates are
used to transform the data to remove the autoregressive nature of the errors.

The second stage uses a bootstrap method to estimate the standard errors of the
parameter estimates of the model. The proposed approach is especially
relevant in the behavioral sciences to analyze small sample timeseries intervention,
but it is appropriate for a wide class of small sample linear model problems in
which there is interest in inferential statements regarding all regression
parameters and autoregressive parameters in the model.

The input format for the data is X:Y matrix. The autocorrelation structure of the error
term must be specified, the default is 1. This can be changed by using the select button below.
You can also specifiy whether or not a bootstrap variance estimate of rho hat is desired. The
default is yes, this can also be changed by selecting a different option below. If a bootstrap
estimate of rho hat is selected, then CI for the AR parameters and the
variancecovariance matrix of AR parameters will be calculated.
Currently the maximum number of parameters is 10 and the maximum sample size
is 300. If you wish to use this software to run a larger model, please contain
Joe McKean via email, joe@stat.wmich.edu.
An example input data set is provided below the submit button.
Example input data set:
1 1 0 0 20
1 2 0 0 18
1 3 0 0 20
1 4 0 0 18
1 5 0 0 21
1 6 0 0 20
1 7 0 0 19
1 8 0 0 22
1 9 0 0 23
1 10 0 0 20

1 11 0 0 22
1 12 0 0 18
1 13 0 0 21
1 14 0 0 19
1 15 0 0 20
1 16 0 0 18
1 17 0 0 20
1 18 0 0 16
1 19 0 0 14
1 20 0 0 18

1 21 1 0 12
1 22 1 1 9
1 23 1 2 6
1 24 1 3 6
1 25 1 4 4
1 26 1 5 6
1 27 1 6 6
1 28 1 7 4
1 29 1 8 2
1 30 1 9 3

1 31 1 10 4
1 32 1 11 4
1 33 1 12 5
1 34 1 13 4
1 35 1 14 2
1 36 1 15 4
1 37 1 16 6
1 38 1 17 4
1 39 1 18 4
1 40 1 19 2
1 41 1 20 2


