𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Robust Depth–Weighted Wavelet for Nonparametric Regression Models

✍ Scribed by Lu Lin

Publisher
Institute of Mathematics, Chinese Academy of Sciences and Chinese Mathematical Society
Year
2005
Tongue
English
Weight
153 KB
Volume
21
Category
Article
ISSN
1439-7617

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Wavelet regression estimation in nonpara
Wavelet regression estimation in nonparametric mixed effect models
✍ Claudia Angelini; Daniela De Canditiis; Frédérique Leblanc 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 510 KB

We show that a nonparametric estimator of a regression function, obtained as solution of a specific regularization problem is the best linear unbiased predictor in some nonparametric mixed effect model. Since this estimator is intractable from a numerical point of view, we propose a tight approximat

Robust designs for Haar wavelet approxim
Robust designs for Haar wavelet approximation models
✍ Xiaojian Xu; Lin Zhao 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 429 KB

In this paper, we discuss the construction of robust designs for heteroscedastic wavelet regression models when the assumed models are possibly contaminated over two different neighbourhoods: G 1 and G 2 . Our main findings are: (1) A recursive formula for constructing D-optimal designs under G 1 ;

Robust model selection in regression via
Robust model selection in regression via weighted likelihood methodology
✍ Claudio Agostinelli 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 130 KB

Robust model selection procedures are introduced as a robust modiÿcation of the Akaike information criterion (AIC) and Mallows Cp. These extensions are based on the weighted likelihood methodology. When the model is correctly speciÿed, these robust criteria are asymptotically equivalent to the class

Robust designs for misspecified exponent
Robust designs for misspecified exponential regression models
✍ Xiaojian Xu 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 161 KB

## Abstract We consider the construction of designs for exponential regression. The response function is an only approximately known function of a specified exponential function. As well, we allow for variance heterogeneity. We find minimax designs and corresponding optimal regression weights in th