A K-fold Averaging Cross-validation Procedure
By Yoonsuh Jung, Jianhua Hu. Journal of nonparametric statistics (Print), 2015
357 citations recorded by the source corpus.
Cross-validation (CV) type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold CV procedure to select a candidate ‘optimal’ model from each hold-out fold and average the K candidate ‘optimal’ models to obtain the ultimate model. Due to the averaging effect, the variance of the proposed estimates can be significantly reduced. This new procedure results in more stable and efficient parameter estimation than the classical K-fold CV procedure. In addition, we show the asymptotic equivalence between the proposed and classical CV procedures in the linear regression setting. We also demonstrate the broad applicability of the proposed procedure via two examples of parameter sparsity regularisation and quantile smoothing splines modelling. We illustrate the promise of the proposed method through simulations and a real data example.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/da1833ce2686dbcc42388993cf8ffc41f59f1133
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Citations in the corpus
- Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning (2020, methodology): “In order tomore accurately evaluate the performance of our proposed SLCABG model, we use 10-fold cross-validation [56] and 52 cross-validation [57] to divide the dataset we use.”