A resampling approach for confidence intervals in linear time-series models after model selection

DOI10.1016/j.physa.2022.128443OpenAlexW4313888461MaRDI QIDQ2683268

Yanyan Li

Publication date: 10 February 2023

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2022.128443

zbMATH Keywords

confidence intervalvariable selectiontime-series model

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glmnet

Cites Work

• A stepwise regression method and consistent model selection for high-dimensional sparse linear models
• Near-ideal model selection by \(\ell _{1}\) minimization
• Estimating the dimension of a model
• Least angle regression. (With discussion)
• Model selection for high-dimensional linear regression with dependent observations
• Simultaneous analysis of Lasso and Dantzig selector
• Coordinate descent algorithms for lasso penalized regression
• Double block bootstrap confidence intervals for dependent data
• A bootstrap recipe for post-model-selection inference under linear regression models
• HYBRID RESAMPLING CONFIDENCE INTERVALS FOR CHANGE-POINT OR STATIONARY HIGH-DIMENSIONAL STOCHASTIC REGRESSION MODELS
• L 1-Regularization Path Algorithm for Generalized Linear Models
• Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems
• Determining the Number of Factors in Approximate Factor Models
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• A new look at the statistical model identification
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A resampling approach for confidence intervals in linear time-series models after model selection