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Assessing the pattern of covariance matrices via an augmentation multiple testing procedure

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  • Francesca Greselin
  • Salvatore Ingrassia
  • Antonio Punzo

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  • Francesca Greselin & Salvatore Ingrassia & Antonio Punzo, 2011. "Assessing the pattern of covariance matrices via an augmentation multiple testing procedure," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(2), pages 141-170, June.
    • Handle: RePEc:spr:stmapp:v:20:y:2011:i:2:p:141-170
    DOI: 10.1007/s10260-010-0157-5
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    References listed on IDEAS

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    1. Martin Crowder, 2008. "Multiple Testing Procedures with Applications to Genomics by Sandrine Dudoit, Mark J. van der Laan," International Statistical Review, International Statistical Institute, vol. 76(2), pages 309-310, August.
    2. Genovese, Christopher R. & Wasserman, Larry, 2006. "Exceedance Control of the False Discovery Proportion," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1408-1417, December.
    3. van der Laan Mark J. & Dudoit Sandrine & Pollard Katherine S., 2004. "Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-27, June.
    4. Mark van der Laan & Sandrine Dudoit & Katherine Pollard, 2004. "Multiple Testing. Part III. Procedures for Control of the Generalized Family-Wise Error Rate and Proportion of False Positives," U.C. Berkeley Division of Biostatistics Working Paper Series 1140, Berkeley Electronic Press.
    5. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
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    Citations

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    Cited by:

    1. Luca Bagnato & Antonio Punzo, 2021. "Unconstrained representation of orthogonal matrices with application to common principal components," Computational Statistics, Springer, vol. 36(2), pages 1177-1195, June.
    2. Maruotti, Antonello & Punzo, Antonio, 2017. "Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 475-496.
    3. Luis Angel García-Escudero & Alfonso Gordaliza & Francesca Greselin & Salvatore Ingrassia & Agustín Mayo-Iscar, 2018. "Eigenvalues and constraints in mixture modeling: geometric and computational issues," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 203-233, June.
    4. Salvatore D. Tomarchio & Luca Bagnato & Antonio Punzo, 2022. "Model-based clustering via new parsimonious mixtures of heavy-tailed distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 315-347, June.
    5. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 363-401, October.
    6. Dariush Najarzadeh & Mojtaba Khazaei & Mojtaba Ganjali, 2015. "Testing for equality of ordered eigenvectors of two multivariate normal populations," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 57-72, April.
    7. Dariush Najarzadeh, 2019. "Testing equality of standardized generalized variances of k multivariate normal populations with arbitrary dimensions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 593-623, December.

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