A permutation test for partial regression coefficients on first-order autocorrelation
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2010
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2553
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eng
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ix, 97 leaves : ill. ; 30 cm.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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National Institute of Development Administration. Library and Information Center
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Pradthana Minsan (2010). A permutation test for partial regression coefficients on first-order autocorrelation. Retrieved from: http://repository.nida.ac.th/handle/662723737/378.
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A permutation test for partial regression coefficients on first-order autocorrelation
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Abstract
This dissertation proposes a permutation test and a permutation
procedure for testing on partial regression coefficients from a multiple linear
regression with first-order autocorrelation where the distribution of the error terms is
not necessarily normal. The proposed permutation procedure can be directly
conducted in the test without having to fit back to the model, which is not the same
procedure as in previous permutation tests, and a proposed permutation test is
considered based on a random permutation test.
In addition, the asymptotic analysis of the proposed test can be obtained when
errors are i.i.d. with mean zero and finite variance. The asymptotic distribution of
, called the asymptotic chi-squared test, can be used to perform a significance test of
partial regression coefficients.
It was found that, for a small sample size (T=12), the proposed permutation
method has the same type I error rate as the partial F-statistic and is not significantly
different from the significance level , and has a higher power when compare with
the other methods in the case where autocorrelation approached . However, with a
moderate sample size (T=16, 20), the asymptotic chi-squared test is preferred (in
terms of type I error and power of the test).
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Thesis (Ph.D. (Statistics))--National Institute of Development Administration, 2010