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Abstract:
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Suppose observations $y_1,\ldots,y_n$ stem from a parametric model $f(y,\theta)$, with the parameter taking one value $\theta_L$ for $y_1,\ldots,y_\tau$ and another value $\theta_R$ for $y_{\tau+1},\ldots,y_n$. I will describe two different general strategies for not merely estimating the break point $\tau$ but also to complement such an estimate with full confidence distributions, both for the change-point $\tau$ and for associated measures of differences between the two levels of $\theta$. The methods will be illustrated for a couple of real data stories, with these meeting different types of challenges.
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