A novel partial-linear single-index model for time series data

2019 ◽  
Vol 134 ◽  
pp. 110-122 ◽  
Author(s):  
Lei Huang ◽  
Hui Jiang ◽  
Huixia Wang
Author(s):  
Nengxiang Ling ◽  
Lilei Cheng ◽  
Philippe Vieu ◽  
Hui Ding

2010 ◽  
Vol 38 (1) ◽  
pp. 246-274 ◽  
Author(s):  
Jane-Ling Wang ◽  
Liugen Xue ◽  
Lixing Zhu ◽  
Yun Sam Chong

2015 ◽  
Vol 43 (1) ◽  
pp. 261-274 ◽  
Author(s):  
Guochang Wang ◽  
Xiang-Nan Feng ◽  
Min Chen

2012 ◽  
Vol 28 (4) ◽  
pp. 1463-1484
Author(s):  
Andrés M. Alonso ◽  
Ana E. Sipols ◽  
Silvia Quintas

2011 ◽  
Vol 39 (6) ◽  
pp. 3441-3443 ◽  
Author(s):  
Ting-Ting Li ◽  
Hu Yang ◽  
Jane-Ling Wang ◽  
Liu-Gen Xue ◽  
Li-Xing Zhu

2021 ◽  
Vol 27 (130) ◽  
pp. 170-184
Author(s):  
Huda Yahya Ahmed ◽  
Munaf Yousif Hmood

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used


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