On model selection from a finite family of possibly misspecified time series models
DOI10.1214/18-AOS1706zbMath1418.62333OpenAlexW2909437707WikidataQ128567386 ScholiaQ128567386MaRDI QIDQ666592
Ching-Kang Ing, Howell Tong, Hsiang-Ling Hsu
Publication date: 6 March 2019
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aos/1547197248
AICBIChigh-dimensional misspecified modelsmisspecification-resistant information criterionmultistep prediction errororthogonal greedy algorithm
Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical aspects of information-theoretic topics (62B10)
Related Items (12)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Regularized estimation in sparse high-dimensional time series models
- A stepwise regression method and consistent model selection for high-dimensional sparse linear models
- VAR forecasting under misspecification
- On the selection of forecasting models
- Cross-validation for selecting a model selection procedure
- Feature matching in time series modeling
- Uniform moment bounds of Fisher's information with applications to time series
- Parametric or nonparametric? A parametricness index for model selection
- Asymptotic properties of criteria for selection of variables in multiple regression
- Approximating data
- Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
- Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
- On predictive least squares principles
- Counterexamples to parsimony and BIC
- Consistent nonparametric regression. Discussion
- Estimating the dimension of a model
- On same-realization prediction in an infinite-order autoregressive process.
- AIC, overfitting principles, and the boundedness of moments of inverse matrices for vector autotregressions and related models.
- Information criteria for selecting possibly misspecified parametric models
- Akaike's information criterion and recent developments in information complexity
- Evaluating panel data forecasts under independent realization
- Order selection for same-realization predictions in autoregressive processes
- Simultaneous analysis of Lasso and Dantzig selector
- Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series
- Forward Regression for Ultra-High Dimensional Variable Screening
- A strongly consistent procedure for model selection in a regression problem
- An optimal selection of regression variables
- Selection of the order of an autoregressive model by Akaike's information criterion
- Generalised information criteria in model selection
- Model Selection and Multimodel Inference
- MULTISTEP PREDICTION IN AUTOREGRESSIVE PROCESSES
- PREDICTION/ESTIMATION WITH SIMPLE LINEAR MODELS: IS IT REALLY THAT SIMPLE?
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Catching up Faster by Switching Sooner: A Predictive Approach to Adaptive Estimation with an Application to the AIC–BIC Dilemma
- MULTISTEP PREDICTION OF PANEL VECTOR AUTOREGRESSIVE PROCESSES
- Some Comments on C P
- Model Selection Principles in Misspecified Models
- Performance bounds for parameter estimates of high-dimensional linear models with correlated errors
- A new look at the statistical model identification
This page was built for publication: On model selection from a finite family of possibly misspecified time series models