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Albert M, Laurent B, Marrel A, Meynaoui A. Adaptive test of independence based on HSIC measures. Ann Stat 2022. [DOI: 10.1214/21-aos2129] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Mélisande Albert
- Institut de Mathématiques de Toulouse; UMR 5219 Université de Toulouse; CNRS, INSA
| | - Béatrice Laurent
- Institut de Mathématiques de Toulouse; UMR 5219 Université de Toulouse; CNRS, INSA
| | - Amandine Marrel
- CEA, DES, IRESNE, DER, Cadarache Center and Institut de Mathématiques de Toulouse; UMR 5219 Université de Toulouse; CNRS
| | - Anouar Meynaoui
- Institut de Mathématiques de Toulouse; UMR 5219 Université de Toulouse; CNRS, INSA
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Zhao Y, Zou C, Wang Z. A scalable nonparametric specification testing for massive data. J Stat Plan Inference 2019. [DOI: 10.1016/j.jspi.2018.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Liu F, Cuenod CA, Thomassin-Naggara I, Chemouny S, Rozenholc Y. Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences. Med Image Anal 2018; 51:125-143. [PMID: 30419490 DOI: 10.1016/j.media.2018.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 10/08/2018] [Accepted: 10/25/2018] [Indexed: 02/07/2023]
Abstract
Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.
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Affiliation(s)
- Fuchen Liu
- Université Paris Descartes, Université Sorbonne Paris Cité (USPC), France; Intrasense(®), France; MAP5, UMR CNRS 8145, France
| | - Charles-André Cuenod
- Université Paris Descartes, Université Sorbonne Paris Cité (USPC), France; Hôpital Européen Georges Pompidou (HEGP), Assistance Publiques - Hôpitaux de Paris (APHP), France; UMR-S970, PARCC, France
| | - Isabelle Thomassin-Naggara
- Université Pierre et Marie Curie - Sorbonne Université, France; Hôpital Tenon - APHP, France; UMR-S970, PARCC, France
| | | | - Yves Rozenholc
- Université Paris Descartes, Université Sorbonne Paris Cité (USPC), France; Faculté de Pharmacie de Paris - EA bioSTM, France.
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Arias-Castro E, Pelletier B, Saligrama V. Remember the curse of dimensionality: the case of goodness-of-fit testing in arbitrary dimension. J Nonparametr Stat 2018. [DOI: 10.1080/10485252.2018.1435875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Ery Arias-Castro
- Department of Mathematics, University of California, San Diego, CA, USA
| | - Bruno Pelletier
- Département de Mathématiques, IRMAR – UMR CNRS 6625, Université Rennes II, Rennes, France
| | - Venkatesh Saligrama
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
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Fromont M, Lerasle M, Reynaud-Bouret P. Family-Wise Separation Rates for multiple testing. Ann Stat 2016. [DOI: 10.1214/15-aos1418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Lack of fit tests for linear regression models with many predictor variables using minimal weighted maximal matchings. J MULTIVARIATE ANAL 2016. [DOI: 10.1016/j.jmva.2016.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Affiliation(s)
- Florian Rohart
- UMR 5219, Institut de Mathématiques de Toulouse, INSA de Toulouse; 135 Avenue de Rangueil 31077 Toulouse cedex 4 France
- UMR 444, Laboratoire de Génétique Cellulaire; INRA Toulouse; 31320 Castanet Tolosan cedex France
- The University of Queensland Diamantina Institute, Translational Research Institute, the University of Queensland; QLD 4102 Australia
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Collier O, Dalalyan AS. Curve registration by nonparametric goodness-of-fit testing. J Stat Plan Inference 2015. [DOI: 10.1016/j.jspi.2015.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Ingster Y, Laurent B, Marteau C. Signal detection for inverse problems in a multidimensional framework. MATHEMATICAL METHODS OF STATISTICS 2015. [DOI: 10.3103/s1066530714040036] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Charbonnier C, Verzelen N, Villers F. A global homogeneity test for high-dimensional linear regression. Electron J Stat 2015. [DOI: 10.1214/15-ejs999] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Marteau C, Mathé P. General regularization schemes for signal detection in inverse problems. MATHEMATICAL METHODS OF STATISTICS 2014. [DOI: 10.3103/s1066530714030028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Huet S, Kuhn E. Goodness-of-fit test for Gaussian regression with block correlated errors. STATISTICS-ABINGDON 2014. [DOI: 10.1080/02331888.2014.913047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Loubes J, Marteau C. Goodness-of-fit testing strategies from indirect observations. J Nonparametr Stat 2013. [DOI: 10.1080/10485252.2013.827680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Fromont M, Laurent B, Reynaud-Bouret P. The two-sample problem for Poisson processes: Adaptive tests with a nonasymptotic wild bootstrap approach. Ann Stat 2013. [DOI: 10.1214/13-aos1114] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Comminges L, Dalalyan AS. Minimax testing of a composite null hypothesis defined via a quadratic functional in the model of regression. Electron J Stat 2013. [DOI: 10.1214/13-ejs766] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Mason DM, Eubank R. Moderate deviations and intermediate efficiency for lack-of-fit tests. STATISTICS & RISK MODELING 2012. [DOI: 10.1524/strm.2012.1116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Abstract
We illustrate a new technique for establishing moderate deviation principles for lack-of-fit tests. The method is applied to the problem of comparing two tests using intermediate asymptotic relative efficiency.
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Affiliation(s)
| | - Randy Eubank
- Arizona State University, Department of Mathematics and Statistics, Tempe, AZ 85287-1804, U.S.A
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Laurent B, Loubes JM, Marteau C. Non asymptotic minimax rates of testing in signal detection with heterogeneous variances. Electron J Stat 2012. [DOI: 10.1214/12-ejs667] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Verzelen N. Minimax risks for sparse regressions: Ultra-high dimensional phenomenons. Electron J Stat 2012. [DOI: 10.1214/12-ejs666] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Verzelen N, Villers F. Goodness-of-fit tests for high-dimensional Gaussian linear models. Ann Stat 2010. [DOI: 10.1214/08-aos629] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Hart JD. Frequentist-Bayes Lack-of-Fit Tests Based on Laplace Approximations. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2009. [DOI: 10.1080/15598608.2009.10411954] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Antoniadis A, Sapatinas T. Estimation and inference in functional mixed-effects models. Comput Stat Data Anal 2007. [DOI: 10.1016/j.csda.2006.09.038] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Fromont M, Lévy-leduc C. Adaptive tests for periodic signal detection with applications to laser vibrometry. ESAIM-PROBAB STAT 2006. [DOI: 10.1051/ps:2006002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Baraud Y, Huet S, Laurent B. Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function. Ann Stat 2005. [DOI: 10.1214/009053604000000896] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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