On the computational complexity of MCMC-based estimators in large samples - Info and Reading Options
By Alexandre Belloni
"On the computational complexity of MCMC-based estimators in large samples" was published by Massachusetts Institute of Technology, Dept. of Economics in 2007 - Cambridge, MA, it has 37 pages and the language of the book is English.
“On the computational complexity of MCMC-based estimators in large samples” Metadata:
- Title: ➤ On the computational complexity of MCMC-based estimators in large samples
- Author: Alexandre Belloni
- Language: English
- Number of Pages: 37
- Publisher: ➤ Massachusetts Institute of Technology, Dept. of Economics
- Publish Date: 2007
- Publish Location: Cambridge, MA
Edition Specifications:
- Pagination: 37 p. :
Edition Identifiers:
- The Open Library ID: OL25480346M - OL16855961W
- Online Computer Library Center (OCLC) ID: 122268578
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"On the computational complexity of MCMC-based estimators in large samples" Description:
The Open Library:
This paper studies the computational complexity of Bayesian and quasi-Bayesian estimation in large samples carried out using a basic Metropolis random walk. The framework covers cases where the underlying likelihood or extremum criterion function is possibly non-concave, discontinuous, and of increasing dimension. Using a central limit framework to provide structural restrictions for the problem, it is shown that the algorithm is computationally efficient. Specifically, it is shown that the running time of the algorithm in large samples is bounded in probability by a polynomial in the parameter dimension d, and in particular is of stochastic order d2 in the leading cases after the burn-in period. The reason is that, in large samples, a central limit theorem implies that the posterior or quasi-posterior approaches a normal density, which restricts the deviations from continuity and concavity in a specific manner, so that the computational complexity is polynomial. An application to exponential and curved exponential families of increasing dimension is given. Keywords: Computational Complexity, Metropolis, Large Samples, Sampling, Integration, Exponential family, Moment restrictions. JEL Classifications: C1, C11, C15, C6, C63.
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