Research Article

A Test for One-sample Repeated Measures Designs: Effect of High-dimensional Data

Boonyarit Choopradit and Samruam Chongcharoen

High-dimensional data, the dimension p of repeated measurements per subject larger than the number n of subjects, are increasingly encountered in various areas of modern science. A test statistic for analyzing high-dimensional one-sample repeated measure designs with no specific form of variance-covariance matrix assumed is proposed. This test statistic asymptotically follows a standard normal distribution for any high dimensional data. Monte Carlo study showed that the proposed test has good power and maintain approximately the nominal level with small n and any large p. Applying the proposed test to the data from body-weight of Wistar rats example with n = 10, p = 22 is demonstrated.

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Boonyarit Choopradit and Samruam Chongcharoen, 2011. A Test for One-sample Repeated Measures Designs: Effect of High-dimensional Data. Journal of Applied Sciences, 11: 3285-3292.

DOI: 10.3923/jas.2011.3285.3292

URL: https://scialert.net/abstract/?doi=jas.2011.3285.3292

Journal of Applied Sciences

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Authors

Boonyarit Choopradit

Samruam Chongcharoen

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Research Article

A Test for One-sample Repeated Measures Designs: Effect of High-dimensional Data

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