An Efron-Stein Inequality for Nonsymmetric Statistics
Loading...
Penn collection
Statistics Papers
Degree type
Discipline
Subject
Efron-Stein inequality
variance bounds
tensor product basis
long common subsequences
Physical Sciences and Mathematics
variance bounds
tensor product basis
long common subsequences
Physical Sciences and Mathematics
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Steele, J Michael
Contributor
Abstract
If S(x1,x2,⋯,xn) is any function of n variables and if Xi,X̂i,1 ≤ i ≤ n are 2n i.i.d. random variables then varS ≤ ½ E ∑i=1n (S - Si)2 where S = S (X1,X2,⋯,Xn) and Si is given by replacing the ith observation with X̂i, so Si=S(X1,X2,⋯,X̂i,⋯,Xn). This is applied to sharpen known variance bounds in the long common subsequence problem.
Advisor
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Publication date
1986-03-01
Journal title
The Annals of Statistics
Volume number
Issue number
Publisher
Publisher DOI
Comments
At the time of publication, author J. Michael Steele was affiliated with Princeton University. Currently, he is a faculty member at the Statistics Department at the University of Pennsylvania.