Abstract #300501

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JSM 2002 Abstract #300501
Activity Number: 357
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #300501
Title: Multi-Step Sequential and Accelerated Sequential Methodologies for a Replicable Linear Model
Author(s): Greg Cicconetti*+ and Mun Son and Nitis Mukhopadhyay and Yong Ko
Affiliation(s): University of Connecticut and University of Vermont and University of Connecticut and University of Vermont
Address: PO BOX 179, Storrs, Connecticut, 06268, USA
Keywords: ellipsoidal confidence regions ; mulit-step acceration ; conditional dispersion matrix ; fixed-maximum diameter ; largest characteristic root ; oversampling rate
Abstract:

We consider a sequence of observable p-dimensional iid normal observations with mean AQ and unknown p.d. variance matrix S* where A is a known matrix of rank q and Q is a q-dimensional vector of unknown regression parameters. We propose new multi-step sequential and accelerated sequential procedures for constructing confidence ellipsoids with maximum diameter less than predetermined length 2d. These procedures, which greatly reduce the "oversampling rate," are compared with their predecessor, a two-stage methodology by Chattergee's (1990). Asymptotic properties of five methods are compared. A large simulation study was conducted to investigate performance under moderate sample sizes.

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