BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:A Hybrid Block Bootstrap For Sample Quantiles Under Weak Dependenc
 e - Alastair Young (Imperial College London)
DTSTART:20180220T110000Z
DTEND:20180220T120000Z
UID:TALK101155@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>The subsampling bootstrap and the moving blocks bootstra
 p provide effective methods for nonparametric inference with weakly depend
 ent data. Both are based on the notion of resampling (overlapping) blocks 
 of successive observations from a data sample: in the former single blocks
  are sampled\, while the latter splices together random blocks to yield bo
 otstrap series of the same length as the original data sample. Here we dis
 cuss a general theory for block bootstrap distribution estimation for samp
 le quantiles\, under mild strong mixing assumptions. A hybrid between subs
 ampling and the moving blocks bootstrap is shown to give theoretical benef
 its\, and startling improvements in accuracy in distribution estimation in
  important practical settings. An intuitive procedure for empirical select
 ion of the optimal number of blocks and their length is proposed. The conc
 lusion that bootstrap samples should be of smaller size than the original 
 data sample has significant implications for computational efficiency and 
 scalability of bootstrap methodologies in dependent data settings. This is
  joint work with Todd Kuffner and Stephen Lee and is described at <a targe
 t="_blank" rel="nofollow" href="https://arxiv.org/abs/1710.02537">https://
 arxiv.org/abs/1710.02537</a>.</span><br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
END:VEVENT
END:VCALENDAR