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STATISTICAL COMPUTING LAB

SCL:
3381 Rood Hall
phone: (269) 387-2852
slab@stat.wmich.edu


Directors:
Dr. Joseph W. McKean
5506 Everett Tower
Department of Statistics
phone: (269) 387-4541
joe@stat.wmich.edu


Dr. John Kapenga
Department of Computer Science
phone: (269) 387-5657
john@cs.wmich.edu


Coodinator:
John Kloke
6610 Everett Tower
Department of Statistics
john.kloke@wmich.edu


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Timeseries Software
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, 87-101 ,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 time-series 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.




Timeseries Input

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 variance-covariance 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 e-mail, joe@stat.wmich.edu.
An example input data set is provided below the submit button.


Data Input
File containing data for analysis:
Select the autocorrelation structure for the error term:
Bootstrap variance estimate of rho hat:

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


Send questions or comments to a slab@stat.wmich.edu

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