Paper Info
Title
Poutine: A correlation estimator for ergodic stationary signals
Abstract
In this work, we present POUTINE, a novel estimator of the auto-correlation function (or more generally, the cross-correlation function) of ergodic stationary signals, an important task in a variety of applications. This estimator sparsely and non-adaptively samples the process via Bernoulli selection, generalizing the classical estimator in a natural way, and offering significant sampling reductions while sacrificing a modest degree of accuracy. Both the mean and variance of our estimator are explicitly analyzed, and in particular, we show that POUTINE gives an unbiased estimate of the classical estimator, which in turn gives an unbiased estimate of the underlying second-order statistics of interest. Furthermore, we show that POUTINE is a consistent estimator with variance approaching zero asymptotically. We demonstrate favorable performance of this approach for a simple stochastic process.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6638897
ICASSP
Keywords
Field
DocType
underlying second-order statistics,non-adaptive measurements,stochastic processes,signal sampling,autocorrelation function,ergodicity,statistical analysis,stochastic processing,unbiased estimation,cross-correlation estimator function,ergodic stationary signal,poutine,nonadaptive sampling reduction,sparsity,adaptive estimation,cross-correlation,bernoulli selection,correlation methods,estimation,reactive power,cross correlation,correlation
Efficient estimator,Minimum-variance unbiased estimator,Mathematical optimization,Stein's unbiased risk estimate,Mean squared error,Stochastic process,Bias of an estimator,Mathematics,Estimator,Consistent estimator
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
2
7
Name
Order
Citations
PageRank
Han Lun Yap1946.66
Aurele Balavoine2143.02
William Mantzel371.72
Ning Tian400.34
Darryl Sale5262.24
Alireza Aghasi6182.10
Justin K. Romberg75856514.08