Abstract
Statistics are derived for tests of changes at unknown times in the parameters of a general linear regression model. Asymptotic distribution theory for the tests is discussed. Simulations are carried out to compare power of the statistics derived in this paper with that of other statistics. The derived statistics are shown to have good power properties as compared to other statistics, particularly for the difficult problem of detecting small changes. The change-point methodology is then applied to data on the incidence of AIDS in the United States.
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