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Order-preserving dimension reduction procedure for the dominance of two mean curves with application to tidal volume curves
Authors:Lee Sang Han  Lim Johan  Vannucci Marina  Petkova Eva  Preter Maurice  Klein Donald F
Institution:Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, New York 10962, U.S.A.;Department of Applied Statistics, Yonsei University, Seoul 120-749, Korea;Department of Statistics, Rice University, Houston, Texas 77251-1892, U.S.A.;Child Study Center, School of Medicine, New York University, New York, U.S.A.;Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, U.S.A.
Abstract:Summary .   The paper here presented was motivated by a case study involving high-dimensional and high-frequency tidal volume traces measured during induced panic attacks. The focus was to develop a procedure to determine the significance of whether a mean curve dominates another one. The key idea of the suggested method relies on preserving the order in mean while reducing the dimension of the data. The observed data matrix is projected onto a set of lower rank matrices with a positive constraint. A multivariate testing procedure is then applied in the lower dimension. We use simulated data to illustrate the statistical properties of the proposed testing procedure. Results on the case study confirm the preliminary hypothesis of the investigators and provide critical support to their overall goal of creating an experimental model of the clinical panic attack in normal subjects.
Keywords:Dimension reduction  Follmann's test  Matrix factorization  Panic disorder  Stochastic order  Tidal volume curves
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