Moment estimation in discrete shifting level model applied to fast array‐CGH segmentation. (29th May 2013)

• Record Type: Journal Article
• Authors: Gandolfi, A.; Benelli, M.; Magi, A.; Chiti, S.
• Is Part Of: Statistica Neerlandica. Volume 67:Number 3(2013:Aug.)
• Page Start: 227
• Page End: 262
• Publication Date: 2013-05-29
• Subjects: Statistics -- Periodicals; 519.5; 314.92
• DOI: 10.1111/stan.12005
• Languages: English
• ISSNs: 0039-0402

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>We develop a mathematical theory needed for moment estimation of the parameters in a general shifting level process (SLP) treating, in particular, the finite state space case geometric finite normal (GFN) SLP. For the SLP, we give expressions for the moment estimators together with asymptotic (co)variances, following, completing, and correcting <sc>Cline</sc> (<italic>Journal of Applied Probability</italic> 20, 1983, 322–337); formulae are then made more explicit for the GFN‐SLP. To illustrate the potential uses, we then apply the moment estimation method to a GFN‐SLP model of array comparative genomic hybridization data. We obtain encouraging results in the sense that a segmentation based on the estimated parameters turns out to be faster than with other currently available methods, while being comparable in terms of sensitivity and specificity.</p> </abstract>