Generalized look-ahead methods for computing stationary densities

R. Anton Braun; Huiyu Li; John Stachurski

doi:10.1287/moor.1120.0547

Generalized look-ahead methods for computing stationary densities

R. Anton Braun^*, Huiyu Li, John Stachurski

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

The look-ahead estimator is used to compute densities associated with Markov processes via simulation. We study a framework that extends the look-ahead estimator to a broader range of applications. We provide a general asymptotic theory for the estimator, where both L1 consistency and L ₂ asymptotic normality are established. The L ₂ asymptotic normality implies pn convergence rates for L ₂ deviation.

Original language	English
Pages (from-to)	489-500
Number of pages	12
Journal	Mathematics of Operations Research
Volume	37
Issue number	3
DOIs	https://doi.org/10.1287/moor.1120.0547
Publication status	Published - Aug 2012

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title = "Generalized look-ahead methods for computing stationary densities",

abstract = "The look-ahead estimator is used to compute densities associated with Markov processes via simulation. We study a framework that extends the look-ahead estimator to a broader range of applications. We provide a general asymptotic theory for the estimator, where both L1 consistency and L 2 asymptotic normality are established. The L 2 asymptotic normality implies pn convergence rates for L 2 deviation.",

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AU - Braun, R. Anton

AU - Li, Huiyu

AU - Stachurski, John

PY - 2012/8

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N2 - The look-ahead estimator is used to compute densities associated with Markov processes via simulation. We study a framework that extends the look-ahead estimator to a broader range of applications. We provide a general asymptotic theory for the estimator, where both L1 consistency and L 2 asymptotic normality are established. The L 2 asymptotic normality implies pn convergence rates for L 2 deviation.

AB - The look-ahead estimator is used to compute densities associated with Markov processes via simulation. We study a framework that extends the look-ahead estimator to a broader range of applications. We provide a general asymptotic theory for the estimator, where both L1 consistency and L 2 asymptotic normality are established. The L 2 asymptotic normality implies pn convergence rates for L 2 deviation.

KW - Densities

KW - Numerical methods

KW - Simulation

UR - https://www.scopus.com/pages/publications/84866688664

U2 - 10.1287/moor.1120.0547

DO - 10.1287/moor.1120.0547

M3 - Article

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VL - 37

SP - 489

EP - 500

JO - Mathematics of Operations Research

JF - Mathematics of Operations Research

IS - 3

ER -

Generalized look-ahead methods for computing stationary densities

Abstract

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