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I'm reading a paper of Shao and Zhang: Testing for Change Points in Time series. In this paper they claim the following: The are testing whether there is a change in the mean of a time series. So

$H_{0}:\mathbb{E}\left[X_{1}\right]=\mathbb{E}\left[X_{2}\right]=\ldots=\mathbb{E}\left[X_{n}\right]=\mu$

$H_{1}:\mathbb{E}\left[X_{1}\right]=\ldots =\mathbb{E}\left[X_{k^{*}}\right]\neq \mathbb{E}\left[X_{k^{*}+1}\right]=\ldots\mathbb{E}\left[X_{n}\right]$

They define the following statistic:

$T_{n}\left(\left[nr\right]\right)=\sum_{\ell=1}^{\left[nr\right]}\left(X_{\ell}-\overline{X_{n}}\right)$ for $r\in\left[0,1\right]$.

They claim that the Sequence $T_{n}\left(\left[nr\right]\right)$ converges to a Brownian Bridge under $H_{0}$ as $n\rightarrow\infty$. But what happens under $H_{1}$ and why?

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Under $H_0$, the sequence of the (random) processes $(T_n(\lfloor n\,\cdot\rfloor)/\sqrt n)_{n=1}^\infty$ (you forgot to divide by $\sqrt n$) converges to the Brownian bridge $B^\circ(\cdot)$ in distribution. Suppose that (as a specification of $H_1$) $h_n\sqrt n\to h\ne0$ and $k^*/n\to\kappa\in(0,1)$, where $h_n:=EX_n-EX_1$. The distribution of the sequence $(X_1,\dots,X_n)$ under $H_1$ is the same as that of the sequence $$(Y_1,\dots,Y_n):=(X_1,\dots,X_{k*},X_{k^*+1}+h_n\dots,X_n+h_n)$$ under $H_0$, so that $Y_j=X_j+h_n I\{j>k^*\}$, where $I$ is the indicator function. So, the distribution of the process $(T_n(\lfloor n\,\cdot\rfloor)/\sqrt n)_{n=1}^\infty$ under $H_1$ is the same as that of of the process $(\tilde T_n(\lfloor n\,\cdot\rfloor)/\sqrt n)_{n=1}^\infty$ under $H_0$, where, with $k:=\lfloor nr\rfloor$,
$$\frac{\tilde T_n(\lfloor nr\rfloor)}{\sqrt n}:=\frac1{\sqrt n}\sum_{j=1}^k(Y_j-\overline{Y})$$ $$=\frac1{\sqrt n}\sum_{j=1}^k(X_j-\overline{X}) +\frac{h_n}{\sqrt n}\sum_{j=1}^k\Big(I\{j>k^*\}-\frac1n\,\sum_{i=1}^n I\{i>k^*\}\Big) $$ $$=\frac1{\sqrt n}\sum_{j=1}^k(X_j-\overline{X}) +h_n \sqrt n\, \psi_{k^*/n}(k/n) $$ $$=\frac{T_n(\lfloor nr\rfloor)}{\sqrt n} +h \psi_\kappa(r)+o(1), $$ where in turn $\psi_u(s):=(u-1)s$ if $0\le s\le u$ and $\psi_u(s):=u(s-1)$ if $u\le s\le 1$.
It follows that, under the mentioned specification of $H_1$, the sequence $(T_n(\lfloor n\,\cdot\rfloor)/\sqrt n)_{n=1}^\infty$ of the processes converges to the process $[0,1]\ni r\mapsto B^\circ(r)+h \psi_\kappa(r)$ in distribution (say in the Skorokhod space $D[0,1]$), where $I$ is the indicator function. Below is the graph of the function $\psi_\kappa$ for $\kappa=0.4$.

enter image description here

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  • $\begingroup$ So it diverges, in general? $\endgroup$
    – bursttim
    Commented Oct 28, 2015 at 14:53
  • $\begingroup$ The full alternative is too broad for one to be able to say something definite about the asymptotics of the distribution. However, if e.g. $h_n\sqrt n\to 0$, then the "alternative" limit distribution is the same as the "null" one (namely, that of the Brownian bridge). So, in this case the hypotheses $H_0$ and $H_1$ are asymptotically indistinguishable. $\endgroup$ Commented Oct 29, 2015 at 3:47
  • $\begingroup$ On the other hand, if e.g. $h_n\sqrt n\to\infty$ (and still $k^*/n\to\kappa\in(0,1)$), then the alternative limit distribution does not exist -- the entire probability mass is being swept away out of the containing space $D[0,1]$, that is, the probability distribution weakly converges to the zero measure on $D[0,1]$. So, in this case the hypotheses $H_0$ and $H_1$ are asymptotically mutually singular and thus highly distinguishable. $\endgroup$ Commented Oct 29, 2015 at 3:47
  • $\begingroup$ I have corrected the calculation of the limit displacement. $\endgroup$ Commented Oct 29, 2015 at 3:48

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