9780243926770 - robust statistics for spatial analysis: the bivariate normal home range model applied to synoptic populations of two species of ground squirrels (classic reprint) di koeppl, j. w. (3 risultati)
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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EUR 24,09
Spedizione gratuitaSpedito in U.S.A.Quantità: 15 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 24,13
EUR 3,84 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 15 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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- Print on Demand
Da: Forgotten Books, London, Regno UnitoForgotten Books
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EUR 15,34
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Paperback. Condizione: New. Print on Demand. This book is an essential resource for researchers and students interested in the robust statistical analysis of spatial data, particularly in the context of animal home ranges. It introduces several robust methods for computing home range parameters such as location, eccentricity, or…ientation, and size, based on bivariate, asymmetrical, iterative trims of the data. The methods have been tested on simulated data and home range data obtained from observations of ground squirrels. The book highlights the non-robust nature of existing bivariate normal home range models and the need for robust alternatives. It demonstrates the effectiveness of the proposed robust methods in minimizing the influence of outliers and providing more reliable estimates of home range parameters. The book contributes to the field of spatial analysis by providing practical and theoretically sound methods for robustly analyzing animal location data. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item.
