تقرير
Variable selection in sparse GLARMA models
| العنوان: | Variable selection in sparse GLARMA models |
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| المؤلفون: | Gomtsyan, M., Lévy-Leduc, C., Ouadah, S., Sansonnet, L. |
| سنة النشر: | 2020 |
| المجموعة: | Statistics |
| مصطلحات موضوعية: | Statistics - Methodology |
| الوصف: | In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our approach consists in iteratively combining the estimation of the autoregressive moving average (ARMA) coefficients of GLARMA models with regularized methods designed for performing variable selection in regression coefficients of Generalized Linear Models (GLM). We first establish the consistency of the ARMA part coefficient estimators in a specific case. Then, we explain how to efficiently implement our approach. Finally, we assess the performance of our methodology using synthetic data and compare it with alternative methods. Our approach is very attractive since it benefits from a low computational load and is able to outperform the other methods in terms of coefficient estimation, particularly in recovering the non null regression coefficients. Comment: arXiv admin note: substantial text overlap with arXiv:1907.07085 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2007.08623 |
| رقم الأكسشن: | edsarx.2007.08623 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |