A copula-based partition Markov procedure

被引：2

作者：

Fernandez, M. ^{[1
]}

Garcia, Jesus E. ^{[2
]}

Gonzalez-Lopez, V. A. ^{[2
]}

机构：

[1] BM&FBOVESPA, Praca Antonio Prado, Sao Paulo, SP, Brazil

[2] Univ Estadual Campinas, Dept Stat, Rua Sergio Buarque de Holanda 651, BR-13083859 Campinas, SP, Brazil

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2018年 / 47卷 / 14期

基金：

巴西圣保罗研究基金会;

关键词：

Bayesian information criterion; copula distribution function; Markov chains; the curse of dimensionality; SELECTION;

D O I：

10.1080/03610926.2017.1359291

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain, and when the size of the database is not large enough, it is not possibly a consistent estimation. In this paper, we introduce a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consists in obtaining a partition of the state space which is constructed from a combination of the partitions corresponding to the marginal processes and the partitions corresponding to the multivariate Markov chain.

引用

页码：3408 / 3417

页数：10

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