Functional Coefficient Regression Models for Non‐linear Time Series: A Polynomial Spline Approach

By Jianhua Z. Huang, Haipeng Shen. 2004

186 citations recorded by the source corpus.

Subfields: Applied Mathematics, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/19232b625ecd85404bf8772d7b783a5c649b699d

How researchers see this paper

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

In Applied Mathematics

1. Timothy M. Christensen
2. Byeong U. Park
3. Liangjun Su
4. Eun Ryung Lee
5. Xiaohong Chen
6. Wenyang Zhang
7. Sainan Jin
8. Xia Wang

In Control and Systems Engineering

1. Jianqing Fan
2. Wenyang Zhang
3. Sainan Jin
4. Xia Wang
5. Xiaohong Chen
6. Rong Liu
7. Young K. Lee
8. Byeong U. Park

In Statistics and Probability

1. Liangjun Su
2. Byeong U. Park
3. Enno Mammen
4. Jianqing Fan
5. Young K. Lee
6. Lijian Yang
7. Eun Ryung Lee
8. Sainan Jin

Citations in the corpus

• Varying Coefficient Regression Models: A Review and New Developments (2015, background): “In Huang & Shen (2004), B-spline sieve estimators were considered and it was shown that they achieve optimal rates.”
• Sieve Estimation of Time-Varying Panel Data Models With Latent Structures (2019, methodology): “1 in Huang and Shen (2004). Specifically, Huang and Shen (2004) prove (i) for  = 2 and strictly stationary strong mixing processes without taking the supremum over  The strict stationarity condition can be relaxed as in Qian and Su (2016).”
• Statistical Methods with Varying Coefficient Models. (2008, methodology): “For polynomial spline approach, the number of knots can be chosen by some commonly used criteria such as CV, AIC, AIC c , BIC and MCV, see Huang et al. (2002, 2004), and Huang and Shen (2004).”
• SPLINE-BACKFITTED KERNEL SMOOTHING OF ADDITIVE COEFFICIENT MODEL (2010, methodology): “Many methods exist for the estimation of functional/varying coefficient models; see Cai, Fan, and Yao (2000) and Yang, Park, Xue, and Härdle (2006) for kernel type estimators; see Huang, Wu, and Zhou (2002) and Huang and Shen (2004) for spline estimators.”
• Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions (2014, methodology): “For example, Huang and Shen (2004) use a similar approach to trim extreme observations in nonparametric functional coefficient regression models, following Tjøstheim and Auestad (1994).”