Application of modified information criterion to multiple change point problems
نویسندگان
چکیده
منابع مشابه
Application of Modified Information Criterion to Multiple Change Point Problems
The modified information criterion (MIC) is applied to detect multiple change points in a sequence of independent random variables. We find that the method is consistent in selecting the correct model, and the resulting test statistic has a simple limiting distribution. We show that the estimators for locations of change points achieve the best convergence rate, and their limiting distribution ...
متن کاملThe Development of an Information Criterion for Change-Point Analysis
Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change point algorithm is used to identify the time indices (change points) at which the system transitions between these discrete states. We present a unified information-based approac...
متن کاملImplied distributions in multiple change point problems
A method for efficiently calculating exact marginal, conditional and joint distributions for change points defined by general finite state Hidden Markov Models is proposed. The distributions are not subject to any approximation or sampling error once parameters of the model have been estimated. It is shown that, in contrast to sampling methods, very little computation is needed. The method prov...
متن کاملParticle Markov Chain Monte Carlo for Multiple Change-point Problems
Multiple change-point models are a popular class of time series models which allow the description of temporal heterogeneity in data. We develop efficient Markov Chain Monte Carlo (MCMC) algorithms to perform Bayesian inference in this context. Our so-called Particle MCMC (PMCMC) algorithms rely on an efficient Sequential Monte Carlo (SMC) technique for change-point models, developed in [13], t...
متن کاملModel Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
The most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood function is proposed as a model selection criterion. This model selection criterion can be used in th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2006
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2006.05.009