Oracally Efficient Two-Step Estimation of Generalized Additive Model
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Publication:5327291
DOI10.1080/01621459.2013.763726OpenAlexW2032412013WikidataQ61865754 ScholiaQ61865754MaRDI QIDQ5327291
Rong Liu, Lijian Yang, Wolfgang Karl Härdle
Publication date: 7 August 2013
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: http://edoc.hu-berlin.de/18452/4958
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Cites Work
- Unnamed Item
- Spline-backfitted kernel smoothing of partially linear additive model
- Rate of convergence in the central limit theorem for random variables with strong mixing
- Efficient and fast spline-backfitted kernel smoothing of additive models
- Optimal estimation in additive regression models
- Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band
- The dimensionality reduction principle for generalized additive models
- Additive regression and other nonparametric models
- Asymptotic maximal deviation of M-smoothers
- Nonparametric statistics for stochastic processes. Estimation and prediction.
- The use of polynomial splines and their tensor products in multivariate function estimation. (With discussion)
- Derivative estimation and testing in generalized additive models
- Direct estimation of low-dimensional components in additive models.
- Nonparametric estimation of an additive model with a link function
- Spline-backfitted kernel smoothing of nonlinear additive autoregression model
- Estimation of semi-parametric additive coefficient model
- Polynomial Spline Estimation for a Generalized Additive Coefficient Model
- Estimation of additive regression models with known links
- Miscellanea. Efficient estimation of additive nonparametric regression models
- Identification of Non-Linear Additive Autoregressive Models
- A kernel method of estimating structured nonparametric regression based on marginal integration
- Nonparametric Autoregression with Multiplicative Volatility and Additive mean
- SPLINE-BACKFITTED KERNEL SMOOTHING OF ADDITIVE COEFFICIENT MODEL
- Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration
- A practical guide to splines.
