First part of this paper deals with tests on the stability of statistical models The problem is formulated in terms of testing the null hypothesis H against the alternative hypothesis A The null hypothesis H claims that the model remains the same during the whole observational period, usually it means that the parameters of the model do not change The alternative hypothesis A claims that, at an unknown time point, the model changes, which means that some of the parameters of the model are subject to a change In case we reject the null hypothesis H, ie when we decide that there is a change in the model, we concentrate on a number of questions that arise: 


when has the model changed;


is there just one change or are there more changes;


what is the total number of changes etc

Additional excerpts

On the asymptotic distribution of a statistic for test of appearance of linear trend

Off-Line Statistical Process Control

Asymptotic distribution of a statistic testing a change in simple linear regression with equidistant design

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On the asymptotic distribution of a statistic for test of appearance of linear trend

Citations

Off-Line Statistical Process Control

Asymptotic distribution of a statistic testing a change in simple linear regression with equidistant design

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