Arbeitspapier
On the computational complexity of MCMC-based estimators in large samples
In this paper we examine the implications of the statistical large sample theory for the computational complexity of Bayesian and quasi-Bayesian estimation carried out using Metropolis random walks. Our analysis is motivated by the Laplace-Bernstein-Von Mises central limit theorem, which states that in large samples the posterior or quasi-posterior approaches a normal density. Using this observation, we establish polynomial bounds on the computational complexity of general Metropolis random walks methods in large samples. Our analysis covers cases, where the underlying log-likelihood or extremum criterion function is possibly nonconcave, discontinuous, and of increasing dimension. However, the central limit theorem restricts the deviations from continuity and log-concavity of the log-likelihood or extremum criterion function in a very specific manner.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP12/07
- Klassifikation
-
Wirtschaft
- Thema
-
Monte Carlo , Computational Complexity , Curved Exponential
Markovscher Prozess
Monte-Carlo-Methode
Mathematik
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Belloni, Alexandre
Chernozhukov, Victor
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2007
- DOI
-
doi:10.1920/wp.cem.2007.1207
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Belloni, Alexandre
- Chernozhukov, Victor
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2007
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