BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Network driven sampling\; a critical threshold for design effects 
 - Karl Rohe (University of Wisconsin-Madison)
DTSTART:20160715T130000Z
DTEND:20160715T133000Z
UID:TALK66776@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Web crawling and respondent-driven sampling (RDS) are two type
 s of network driven sampling techniques that are popular when it is diffic
 ult to contact individuals in the population of interest. This paper studi
 es network driven sampling as a Markov process on the social network that 
 is indexed by a tree. Each node in this tree corresponds to an observation
  and each edge in the tree corresponds to a referral. Indexing with a tree
 \, instead of a chain\, allows for the sampled units to refer multiple fut
 ure units into the sample. In survey sampling\, the design effect characte
 rizes the additional variance induced by a novel sampling strategy. If the
  design effect is $D$\, then constructing an estimator from the novel desi
 gn makes the variance of the estimator $D$ times greater than it would be 
 under a simple random sample. Under&nbsp\;certain assumptions on the refer
 ral tree\, the design effect of network driven sampling has a critical thr
 eshold that is a function of the referral rate $m$ and the clustering stru
 cture in the social network\, represented by the second eigenvalue of the 
 Markov transition matrix $\\lambda_2$. If $m < 1/\\lambda_2^2$\, then the 
 design effect is finite (i.e. the standard estimator is $\\sqrt{n}$-consis
 tent). However\, if $m > 1/\\lambda_2^2$\, then the design effect grows wi
 th $n$ (i.e. the standard estimator is no longer $\\sqrt{n}$-consistent\; 
 it converges at the slower rate of $\\log_m \\lambda_2$).
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR