Statistical properties of positively-censored estimators of herbaceous primary productivity derived from successive independent surveys

Statistical properties of positively-censored estimators of herbaceous primary productivity derived from successive independent surveys

The estimation of herbaceous primary productivity is usually derived from successive independent surveys. In order to avoid negative estimates, ecologists often use positively-censored estimators, i.e. they keep positive values but replace negative values by zero. The statistical properties of these...

Full description

Saved in:
Bibliographic Details
Published in:Acta oecologica (Montrouge) Vol. 19; no. 1; pp. 61 - 66
Main Authors: Houllier, Françoise, Shridhar, Devidas, Hervé, Jean-Christophe, Puyravaud, Jean-Philippe
Format: Journal Article
Language:English
Published: Paris Elsevier Masson SAS 01-01-1998
Elsevier
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
Synecology
Terrestrial ecosystems
Asia
India
Case study
Estimation error
Primary productivity
Accuracy
Statistical analysis
Herbaceous layer
Savannah
Sampling
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The estimation of herbaceous primary productivity is usually derived from successive independent surveys. In order to avoid negative estimates, ecologists often use positively-censored estimators, i.e. they keep positive values but replace negative values by zero. The statistical properties of these positively-censored estimators are analysed in this paper: their relative bias, variance and mean squared error can be expressed as functions of the sampling coefficient of variation (i.e. the ratio between the average sampling error and the true unknown productivity). When this coefficient increases, bias increases while variance and mean squared error decrease. A case study from a south Indian savanna illustrates that the bias is not negligible, especially for the months of low herbaceous productivity. In the case of normal distributions, analytical formula are derived, which can further be used to design sampling strategies that optimize their accuracy and precision.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1146-609X
1873-6238
DOI:10.1016/S1146-609X(98)80008-8