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Title: Lugsail lag windows and their application to MCMC Authors:  Dootika Vats - Indian Institute of Technology, Kanpur (India) [presenting]
James Flegal - University of California - Riverside (United States)
Abstract: Lag windows are commonly used in the time series, steady state simulation, and Markov chain Monte Carlo literature to estimate the long range variances of estimators arising from correlated data. We propose a new lugsail lag window specially designed to yield biased from above estimators for the long range variances. We use this lag window for batch means and spectral variance estimators in Markov chain Monte Carlo simulations and establish conditions ensuring strong consistency and mean square consistency. Further, we calculate the bias and variance of lugsail estimators and demonstrate there is little loss compared to other estimators. Finally, we study the finite sample properties of lugsail estimators in various examples.