Identification of non‐linear additive autoregressive models

By Jianhua Z. Huang, Lijian Yang. 2004

116 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f013d997ce250710a3fbb2ca403ba43f7c7dabd9

How researchers see this paper

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

In Control and Systems Engineering

1. Li Wang
2. Shujie Ma
3. Tianjiao Chu
4. Clark Glymour
5. Annie Qu
6. Jianhui Zhou
7. Lan Xue
8. Jeffrey S. Racine

In Statistics and Probability

1. Shujie Ma
2. Li Wang
3. Timothy M. Christensen
4. Lijian Yang
5. Lan Xue
6. Rong Liu
7. Jeffrey S. Racine
8. Jianhui Zhou

Citations in the corpus

• Spline Regression in the Presence of Categorical Predictors (2015, methodology): “For applications of spline approaches, see Huang (1998) for functional ANOVA models, Huang and Yang (2004), Wang and Yang (2007) and Xue (2009) for additive models, Wang and Yang (2009) and Wang (2009) for single-index models, * Correspondence to: Jeffrey S. Racine, Department of Economics, Kenneth…”
• SPLINE ESTIMATION OF SINGLE-INDEX MODELS (2009, methodology): “Using BIC similar to Huang and Yang (2004) for our model with three interior knots, the following nine explanatory variables were selected from the above set {Y t−1 , . . . , Y t−4 , X t , X t−1 , X t−2 , Z t , Z t−1 }.”
• Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data (2010, methodology): “Other approaches to variable selection in additive models include those of Huang and Yang (2004) and Xue (2009).”
• Search for Additive Nonlinear Time Series Causal Models (2008, methodology): “For example, to test if Xt−1 is present in E[Xt |L,Zt ], we follow Huang and Yang (2004) by starting from a model A, where Xt is regressed against L and Zt , and searching for a submodel of A with the lowest BIC score.”
• Spline-backfitted kernel smoothing of nonlinear additive autoregression model (2006, methodology): “Some of these are the kernel estimators via marginal integration of [25, 29], and the spline estimators of [14].”
• Spline-backfitted kernel smoothing of partially linear additive model (2011, unknown): “Since the 1980s, non-and semiparametric analysis of time series has been vigorously pursued, see, for example, Tjøstheim and Auestad (1994) and Huang and Yang (2004).”
• SPLINE-BACKFITTED KERNEL SMOOTHING OF ADDITIVE COEFFICIENT MODEL (2010, background): “Spline approaches of Stone (1985), Huang (1998a, 1998b), and Huang and Yang (2004) to the additive model, on the other hand, do not satisfy criterion (ii), as they lack limiting distribution but are fast to compute, thus satisfying (i).”
• Tuning parameter selectors for the smoothly clipped absolute deviation method. (2007, unknown): “Shi & Tsai (2002, 2004) and Huang & Yang (2004) .”
• Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions (2014, background): “Huang and Yang (2004) and others derived the optimal L2 rate for spline LS regression with strongly mixing sequence assuming a uniformly bounded higher-than-second conditional moment.”