Size-biased random closed sets

M.N.M. Lieshout, van

doi:10.1016/S0031-3203(99)00025-4

Size-biased random closed sets

M.N.M. Lieshout, van

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

We indicate how granulometries may be useful in the analysis of random sets. We define a suitable size distribution function as a tool in exploratory data analysis and give a new Hanisch style estimator for it. New Markov random sets are constructed which favour certain sizes above others. A size-biased random set model is fitted to a data set concerning the incidence of heather (Diggle, Biometrics 37 (1981) 531–539).

Original language	English
Pages (from-to)	1631-1644
Number of pages	14
Journal	Pattern Recognition
Volume	32
Issue number	9
DOIs	https://doi.org/10.1016/S0031-3203(99)00025-4
Publication status	Published - 1999

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title = "Size-biased random closed sets",

abstract = "We indicate how granulometries may be useful in the analysis of random sets. We define a suitable size distribution function as a tool in exploratory data analysis and give a new Hanisch style estimator for it. New Markov random sets are constructed which favour certain sizes above others. A size-biased random set model is fitted to a data set concerning the incidence of heather (Diggle, Biometrics 37 (1981) 531–539).",

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AB - We indicate how granulometries may be useful in the analysis of random sets. We define a suitable size distribution function as a tool in exploratory data analysis and give a new Hanisch style estimator for it. New Markov random sets are constructed which favour certain sizes above others. A size-biased random set model is fitted to a data set concerning the incidence of heather (Diggle, Biometrics 37 (1981) 531–539).

U2 - 10.1016/S0031-3203(99)00025-4

DO - 10.1016/S0031-3203(99)00025-4

M3 - Article

SN - 0031-3203

VL - 32

SP - 1631

EP - 1644

JO - Pattern Recognition

JF - Pattern Recognition

IS - 9

ER -

Size-biased random closed sets

Abstract

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