共查询到4条相似文献,搜索用时 0 毫秒
1.
Zsolt Talata 《Periodica Mathematica Hungarica》2005,51(1):99-117
Summary This is a survey of the information criterion approach to model selection problems. New results about context tree estimation
and the estimation of the basic neighborhood of Markov random fields are also mentioned. 相似文献
2.
Mariela Fernández Jesús E. García Ramin Gholizadeh Verónica A. González-López 《Mathematical Methods in the Applied Sciences》2020,43(13):7537-7549
In this paper, we propose a procedure of selecting samples from a set of samples coming from Markovian processes of finite order and finite alphabet. Under the assumption of the existence of a law that prevails in at least q% of the samples of the collection, we show that the procedure allows to identify samples governed by the predominant law. The approach is based on a local metric between samples, which tends to zero when we compare samples of identical law and tends to infinity when comparing samples with different laws. The local metric allows to define a criterion which takes arbitrarily large values when the previous assumption about the existence of a predominant law does not hold. By means of this procedure, we map similarities and dissimilarities of some Brazilian stocks' daily trading volume dynamic. 相似文献
3.
Learning locally minimax optimal Bayesian networks 总被引:1,自引:0,他引:1
4.
Akaike Information Criterion (AIC) is frequently employed in the semiparametric setting of selection of copula models, even though as a model selection tool it was developed in a parametric setting. Recently a Copula Information Criterion (CIC) has been especially designed for copula model selection. In this paper we examine the two approaches and present a simulation study where the performance of a cross-validated version of CIC is compared with the AIC criterion. Only minor differences are observed. 相似文献