𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Jackknifing type weighted least squares estimators in partially linear regression models

✍ Scribed by Jinhong You; Xiaoqian Sun; Wan-kai Pang; Ping-kei Leung

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
189 KB
Volume
60
Category
Article
ISSN
0167-7152

No coin nor oath required. For personal study only.

✦ Synopsis

In a heteroskedastic partially linear regression model, You and Chen (

πŸ“œ SIMILAR VOLUMES

A paradox in least-squares estimation of
A paradox in least-squares estimation of linear regression models
✍ Z.D. Bai; Meihui Guo πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 97 KB

This note considers a paradox arising in the least-squares estimation of linear regression models in which the error terms are assumed to be i.i.d. and possess ÿnite rth moment, for r ∈ [1; 2). We give a concrete example to show that the least-squares estimator of the slope parameter is inconsistent

Ambient error weighted partial least-squ
Ambient error weighted partial least-squares regression: a new receptor model
✍ Shizoom Wang; Timothy Larson πŸ“‚ Article πŸ“… 1993 πŸ› Elsevier Science 🌐 English βš– 464 KB

In the proposed model, both the amblent data and the source profiles were weqbted by the amblent mass scaled amblent error The comparison between thts model and the effectwe varrance we&ted chemtcal mass balance-type model was done usmg NBS test data sets The compared results show that the amblent e