BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME;VALUE=TEXT:Statistics Colloquium Series
PRODID:-//Harvard events data//EN
BEGIN:VEVENT
UID:event_1432347_0
SUMMARY:Statistics Colloquium Series
DESCRIPTION:<p>	Our next event for the Statistics Department Colloquium Series is scheduled for this Monday, March 21st from 12:00 – 1:00pm (ET) and will be an in person presentation in room 316 of the Science Center. The speaker is Yuan Liao who is an Associate Professor of Economics at Rutgers University.</p><p>	<strong>Title</strong> : Neural Network Inference on Nonparametric Conditional Moment Restrictions with Weakly Dependent Data</p><p>	<strong>Abstract </strong>: <span style="background:white"><span style="color:black">Neural Networks (NNs) are nonlinear sieves that can approximate nonlinear functions</span></span> <span style="background:white"><span style="color:black">of high dimensional variables much more flexibly than various linear sieves (or series).</span></span> <span style="background:white"><span style="color:black">This paper considers quasi-likelihood ratio (NN-QLR) based inference on expectation</span></span> <span style="background:white"><span style="color:black">functionals of time series data, where the functionals of interest are based on some</span></span> <span style="background:white"><span style="color:black">nonparametric function that satisfy conditional moment restrictions, and are learned</span></span> <span style="background:white"><span style="color:black">using multilayer neural networks. While the asymptotic normality of the estimated</span></span> <span style="background:white"><span style="color:black">functionals depends on some unknown Riesz representer of the functional space, we</span></span> <span style="background:white"><span style="color:black">show that the NN-QLR statistic is asymptotically Chi-square distributed, regardless</span></span> <span style="background:white"><span style="color:black">whether the expectation functional is regular (root- n estimable) or not. This holds</span></span> <span style="background:white"><span style="color:black">when the data are weakly dependent and satisfy the beta-mixing condition. We apply our method to the off-policy evaluation in reinforcement learning, by formulating the Bellman equation into the conditional moment restriction framework, so that we can make inference about the state-specific value functional using the proposed NN-QLR method with time series data.</span></span> <span style="background:white"><span style="color:black">In addition, estimating the averaged partial means and averaged partial derivatives of</span></span> <span style="background:white"><span style="color:black">nonparametric instrumental variables and quantile IV models are also presented as leading examples.</span></span> <span style="background:white"><span style="color:black">Finally, a Monte Carlo study shows the finite sample performance of the procedure.</span></span></p><p>	<strong>Bio</strong>: Y<span style="background:#f9f9f9"><span style="color:black">uan Liao is Associate Professor of Economics at Rutgers University. He received his Ph.D. in Statistics from Northwestern University in 2010. Before joining Rutgers, Yuan held a position as Assistant Professor at University of Maryland, and worked at Princeton University as a postdoctoral associate. He serves as the Associate Editor for Journal of Econometrics since 2020.</span></span></p><p>	<strong>Please note</strong>: The Spring 2022 Colloquium (Stat 314) series will be held interchangeably virtually on Zoom or in person. Zoom events will be recorded, and retained for a short period, solely for students enrolled in Stat 314 and Harvard Statistics Department members. If you do not wish to have your video recorded, you are welcome to turn off your video feed during the talk.</p>
LOCATION:Science Center, Science Hall E
STATUS:CONFIRMED
DTSTART:20220321T160000Z
DTEND:20220321T170000Z
END:VEVENT
END:VCALENDAR