Selection of Regression and Autoregression Models with Initial Ordering of Variables

DOI10.1080/03610926.2011.573161zbMath1258.62077OpenAlexW2059738025MaRDI QIDQ4904704

Jan Mielniczuk, Paweł Teisseyre

Publication date: 31 January 2013

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2011.573161

zbMATH Keywords

consistencyprediction errorpenaltygeneralized information criterionAkaike and Schwarz ruleregression and autoregression

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05)

Cites Work

• On the choice of a model to fit data from an exponential family
• Asymptotic properties of projections with applications to stochastic regression problems
• Estimating the dimension of a model
• Model selection under nonstationarity: Autoregressive models and stochastic linear regression models
• Bootstrap and wild bootstrap for high dimensional linear models
• Statistical predictor identification
• Selection of the order of an autoregressive model by Akaike's information criterion

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