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<p>Dear all,</p>
<p>On Thursday (14 February) at 2pm in <span class="event-where">54
/ 7035 (7B), we have an S3RI seminar from Yi Yu (University of
Bristol) on </span><span class="event-where"><span
class="event-description">"</span></span><span
class="event-where"><span class="event-description"><span
class="event-description"><span class="event-description">Univariate
Mean Change Point Detection: Penalization, CUSUM and
Optimality</span></span>". Details are given below.<br>
</span></span></p>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description">The seminar will also be
available via a live web-cast at</span></span></span></span><br>
<span class="event-description"><a class="moz-txt-link-freetext" href="https://coursec">https://coursec</a><wbr>ast.soton.ac.uk<wbr>/Panopto/Pages/<wbr>Viewer.aspx?id=<wbr>8b1c58e7-6289-4<wbr>a89-90f9-a9f100<wbr>8d9bc1</span><br>
</p>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description">The talk will be followed by tea
and cake in the staff reading room on level 4 of building
54. </span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description">All are welcome!</span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Best
wishes,</span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Helen</span></span></span></p>
<p><span class="event-details-label"></span><span
class="event-description">Univariate Mean Change Point
Detection: Penalization, CUSUM and Optimality <br>
</span></p>
<p><span class="event-description">Yi Yu, University of Bristol <br>
</span></p>
<p><span class="event-description">The problem of univariate mean
change point detection and localization based on a sequence of n
independent observations with piecewise constant means has been
intensively studied for more than half century, and serves as a
blueprint for change point problems in more complex settings. We
provide a complete characterizatio<wbr>n of this classical
problem in a general framework in which the upper bound on the
noise variance $\sigma^2$, the minimal spacing ∆ between two
consecutive change points and the minimal magnitude of the
changes κ, are allowed to vary with n. We first show that
consistent localization of the change points when the
signal-to-noise ratio $\frac{\kappa \sqrt{\Delta}}{<wbr>\sigma}$
is uniformly bounded from above is impossible. In contrast, when
$\frac{\kappa \sqrt{\Delta}}{<wbr>\sigma}$ is diverging in $n$
at any arbitrary slow rate, we demonstrate that two
computationally<wbr>-efficient change point estimators, one
based on the solution to an $\ell_0$-penali<wbr>zed least
squares problem and the other on the popular WBS algorithm, are
both consistent and achieve a localization rate of the order
$\frac{\sigma^2<wbr>}{\kappa^2} \log(n)$. We further show that
such rate is minimax optimal, up to a log(n) term. <br>
</span></p>
<p><span class="event-description">
<a class="moz-txt-link-freetext" href="https://arxiv.o"><font color="red"><b>MailScanner has detected a possible fraud attempt from "arxiv.o" claiming to be</b></font> https://arxiv.o</a><wbr>rg/abs/1810.094<wbr>98</span></p>
<p><span class="event-description"><br>
</span></p>
<span class="event-where"><span class="event-description"><span><span
class="event-description"><span> <span
class="event-description"> </span><span
class="event-description"><span
class="event-description">For the current schedule of
S3RI seminars, see <a class="moz-txt-link-freetext"
href="https://tinyurl.com/s3riseminar">https://tinyurl.com/s3riseminar</a>
</span></span></span></span></span></span></span><br>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description"></span></span></span></span></p>
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