Magnussen, Steen

[["dc.bibliographiccitation.firstpage","213"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","European Journal of Forest Research"],["dc.bibliographiccitation.lastpage","224"],["dc.bibliographiccitation.volume","127"],["dc.contributor.author","Magnussen, S."],["dc.contributor.author","Kleinn, C."],["dc.contributor.author","Picard, N."],["dc.date.accessioned","2017-09-07T11:47:11Z"],["dc.date.available","2017-09-07T11:47:11Z"],["dc.date.issued","2008"],["dc.description.abstract","Two new density estimators for k-tree distance sampling are proposed and their performance is assessed in simulated distance sampling from 22 stem maps representing a wide range of natural to semi-natural forest tree stands with random to irregular (clustered) spatial distribution of trees. The new estimators are model-based. The first (Orbit) computes density as the inverse of the average of the areas associated with each of the k-trees nearest to a sample location. The area of the k-th tree is obtained as a prediction from a linear regression model while the area of the first is obtained via a Poisson probability integral. The second (GamPoi) is based on the expected distribution of distance to the k nearest tree in a forest where the local distribution of trees is random but the stem density varies from sample location to sample location as a gamma distribution. In a comprehensive assessment with 17 promising reference estimators, a subset composed of Morisita’s, Persson’s, Byth’s, Kleinn’s, Orbit, and GamPoi was significantly better, in terms of relative root mean square error (RRMSE), than average. GamPoi emerged as the better estimator for sample sizes larger than or equal to 30. For smaller sample sizes, both Kleinn’s and Morisita’s appear attractive."],["dc.identifier.doi","10.1007/s10342-007-0197-z"],["dc.identifier.gro","3149290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5950"],["dc.language.iso","en"],["dc.notes.intern","Kleinn Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1612-4669"],["dc.title","Two new density estimators for distance sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","429"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Forest Science"],["dc.bibliographiccitation.lastpage","441"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Picard, N."],["dc.contributor.author","Kleinn, Christoph"],["dc.date.accessioned","2017-09-07T11:48:56Z"],["dc.date.available","2017-09-07T11:48:56Z"],["dc.date.issued","2008"],["dc.identifier.gro","3149560"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6242"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.title","A gamma-Poisson distribution of the point to the k nearest event distance"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","429"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Forest Science"],["dc.bibliographiccitation.lastpage","441"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Picard, N."],["dc.contributor.author","Kleinn, Christoph"],["dc.date.accessioned","2018-11-07T11:12:27Z"],["dc.date.available","2018-11-07T11:12:27Z"],["dc.date.issued","2008"],["dc.description.abstract","Distance sampling of events in natural or seminatural populations often indicates a larger variance in the distance to the kth nearest event than expected for events distributed completely at random. Overdispersion contributes to the well-known bias problem of distance sampling density estimators. Distance distribution models that accommodate overdispersion in the data should lead to more robust estimators of density. To this end we propose a gamma-Poisson distribution model for distances from a point to k nearest events. The model assumes a gamma distribution of local densities of randomly distributed events. Properties of the distribution and estimation of the parameters and event density are detailed for both constrained and unconstrained sampling. Four examples, one with simulated data from a known negative binomial distribution and three with simulated distance sampling in natural and seminatural stem-mapped tree stands, illustrate the promising performance of this new distribution, both as a model for distances and for density estimation. The modeling approach extends to other mixing distributions."],["dc.identifier.isi","000258282000005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/53669"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1938-3738"],["dc.relation.issn","0015-749X"],["dc.title","A gamma-Poisson distribution of point to k nearest event distance"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]