Ridge estimation of a semiparametric regression model

By Hongchang Hu. 2005

50 citations recorded by the source corpus.

Considered the semiparametric regression model li = AiTX + s(ti) + Δi (i = 1, 2,...,n). Firstly, ridge estimators of both parameters and nonparameters are attained without a restrained design matrix. Secondly, the ridge estimator will be compared with two steps estimation under a mean square error and some conditions in which the former excels the latter are given. Finally, the validity and feasibility of the method are illustrated by a simulating example.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/59a44697e3bbfbe5ff3c84a2d9ceaa35a6209b61

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gülin Tabakan
2. Fikri Akdeniz

In Control and Systems Engineering

1. Gülin Tabakan
2. Fikri Akdeniz

In Statistics and Probability

1. Gülin Tabakan
2. Fikri Akdeniz
3. Hu Yang

Citations in the corpus

• Restricted Ridge Estimators of the Parameters in Semiparametric Regression Model (2009, methodology): “Recently, Hu ( 2005 ) proposed ridge estimation of semiparametric regression model and introduced a ridge estimation method based on a two-step estimation process.”