NDLI: Nonparametric prediction in species sampling

Content Provider	Springer Nature Link
Author	Chao, Anne Shen, Tsung Jen
Copyright Year	2004
Abstract	Consider a continuous-time stochastic model in which species arrive in the sample according to independent Poisson processes and where the species discovery rates are heterogeneous. Based on an initial survey, we are concerned with the problem of predicting the number of new species that would be discovered by additional sampling. When the sampling time or sample size of the additional sample tends to infinity, this problem reduces to the prediction of the number of undetected species in the original sample, or equivalently, the estimation of species richness. The topic has a wide range of applications in various disciplines. We propose a simple prediction method and apply it to two datasets. One set of data deals with the capture counts of the Malayan butterfly and the other set deals with identification records of organic pollutants in a water environment. Simulation results are shown to investigate the performance of the proposed method and to compare it with the existing estimators.
Starting Page	253
Ending Page	269
Page Count	17
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	9
Issue Number	3
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2004-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discovery rates Frequency counts Species abundance Species richness Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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