Name
Abstract | ||
|---|---|---|
We study reflected standardized time series (STS) estimators for the asymptotic variance parameter of a stationary stochastic process. These estimators are based on the concept of data re-use and allow us to obtain more information about the process with no additional sampling effort. Reflected STS estimators are computed from “reflections” of the original sample path. We show that it is possible to construct linear combinations of reflected estimators with smaller variance than the variance of each constituent estimator, often at no cost in bias. We provide Monte Carlo examples to show that the estimators perform as well in practice as advertised by the theory. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WSC.2010.5679063 | Winter Simulation Conference |
Keywords | Field | DocType |
stochastic processes,original sample path,reflected variance estimators,parameter estimation,reflected sts estimator,reflected variance estimator,monte carlo example,simulation,data re-use,monte carlo methods,linear combination,constituent estimator,stationary stochastic process,standardized time series estimators,asymptotic variance parameter,smaller variance,time series,additional sampling effort,monte carlo examples,modeling,monte carlo,time series analysis,random variables,stochastic process,limiting,convergence,asymptotic variance | Extremum estimator,Monte Carlo method,Random variable,Stochastic process,Bootstrapping (statistics),Estimation theory,Statistics,Delta method,Mathematics,Estimator | Conference |
Volume | Issue | ISSN |
47 | 11 | 0891-7736 |
ISBN | Citations | PageRank |
978-1-4244-9866-6 | 2 | 0.40 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Melike Meterelliyoz | 1 | 11 | 2.32 |
| Christos Alexopoulos | 2 | 426 | 77.68 |
| David Goldsman | 3 | 904 | 159.71 |