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Bootstrap Confidence Intervals Using the Likelihood Ratio Test in Changepoint Detection by Chen, Ryan; Cabrera, Javier is a scholarly article available to read on EtoBox.

What is Bootstrap Confidence Intervals Using the Likelihood Ratio Test in Changepoint Detection about?

This study aims to evaluate the performance of power in the likelihood ratio test for changepoint detection by bootstrap sampling, and proposes a hypothesis test based on bootstrapped confidence interval lengths. Assuming i.i.d normally distributed errors, and using the bootstrap method, the changepoint sampling distribution is estimated. Furthermore, this study describes a method to estimate a data set with no changepoint to form the null sampling distribution. With the null sampling distribution, and the distribution of the estimated changepoint, critical values and power calculations can be made, over the lengths of confidence intervals.

Author

Chen, Ryan; Cabrera, Javier

Published

2020

Language

EN