𝔖 Bobbio Scriptorium
✦   LIBER   ✦

ModelingBromus diandrusSeedling Emergence Using Nonparametric Estimation

✍ Scribed by Cao, R.; Francisco-Fernández, M.; Anand, A.; Bastida, F.; González-Andújar, J. L.

Book ID
118242364
Publisher
Springer-Verlag
Year
2012
Tongue
English
Weight
452 KB
Volume
18
Category
Article
ISSN
1085-7117

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Pseudo-empirical likelihood estimation u
Pseudo-empirical likelihood estimation using nonparametric regression
✍ M. Rueda; J.F. Muñoz 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 458 KB

Pseudo-empirical likelihood estimation of the population mean is considered. A nonparametric regression theory is proposed, to provide the fitted values on which to calibrate, and the common model misspecification problem is therefore addressed. Results derived from empirical studies show that the p

Nonparametric tail estimation using a do
Nonparametric tail estimation using a double bootstrap method
✍ Jef Caers; Jozef Van Dyck 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 249 KB

Extreme value theory has led to the development of various statistical methods for nonparametric estimation of distribution tails. A common problem in all of these estimators is the choice of the number of extreme data that should be used in the estimation and the construction of conÿdence intervals