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Random Projections: Applications to Statistical Data Depth and Goodness of Fit Test

  • Autores: Alicia Nieto Reyes Árbol académico
  • Localización: BEIO, Boletín de Estadística e Investigación Operativa, ISSN 1889-3805, Vol. 35, Nº. 1, 2019, págs. 7-22
  • Idioma: inglés
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  • Referencias bibliográficas
    • [1] Azencott, R. y Dacunha-Castelle, D. (1986).Series of IrregularObservations: Forecasting and Model Building. Springer.
    • [2] Benjamini, Y. y Hochberg, Y. (1995). Controlling the false discovery rate:a practical and powerful approach to multiple testing.J. Roy....
    • [3] Benjamini, Y. y Yekutieli, D. (2001). The control of the false discovery ratein multiple testing under dependency.Ann. Statist.,29(4),...
    • [4] Bugni, F.A., Hall, P., Horowitz, J.L. y Neumann G.R. (2009).Goodness-of-fit tests for functional data.Econom. J.,12, S1–S18.
    • [5] Cuesta-Albertos, J.A., del Barrio, T., Fraiman, R. y Matr ́an, C. (2007). Therandom projection method in goodness of fit for functional...
    • [6] Cuesta-Albertos, J.A., Cuevas, A. y Fraiman, R. (2009). Onprojection-based tests for spherical and compositional data.Stat.Comput.,19(4),...
    • [7] Cuesta-Albertos, J.A. y Febrero-Bande, M. (2010). Multiway ANOVA forFunctional Data.TEST,19, 537-557.
    • [8] Cuesta-Albertos J.A., Fraiman R., Galves A., Garcia J. y Svarc, M.(2007). Classifying speech sonority functional data using a projectedKolmogorov-Smirnov...
    • [9] Cuesta-Albertos, J.A., Fraiman, R. y Ransford, T. (2006) Randomprojections and goodness-of-fit tests in infinite-dimensional spaces.Bull.Braz....
    • [10] Cuesta-Albertos J.A., Fraiman R. y Ransford T. (2007). A sharp form ofthe Cram ́er-Wold theorem.J. Theoret. Probab.,20, 201–209.
    • [11] Cuesta-Albertos J.A. y Nieto-Reyes, A. (2008). The random Tukey depth.Comput. Statist. Data Anal.,52(11), 4979–4988.
    • [12] Cuesta-Albertos J.A. y Nieto-Reyes, A. (2008). The Tukey and the randomTukey depths characterize discrete distributions.J. Multivariate...
    • [13] Cuesta-Albertos J.A. y Nieto-Reyes, A. (2008). A random functional depth.InFunctional y Operational Statistics. Eds. S. Dabo-Niang y...
    • [14] Cuevas, A., Febrero-Bande, M. y Fraiman, R. (2007). Robust estimationand classification for functional data via projection-based depth...
    • [15] Cuevas, A. y Fraiman, R. (2009). On depth measures and dual statistics. Amethodology for dealing with general data.J. Multivariate Anal.,100(4),753–766.
    • [16] Epps, T. W. (1987). Testing that a stationary time series is Gaussian.Ann.Statist.,15(4), 1683–1698.
    • [17] Escanciano, J.C. (2006). A consistent diagnostic test for regression modelsusing projections.Econometric Theory,22, 1030-1051.
    • [18] Febrero-Bande, M., Galeano, P. y Gonz ́alez-Manteiga, W. (2008). Outlierdetection in functional data band depth measures with application...
    • [19] Fox, J.T. y Gyhi A. (2011). Identifying heterogeneity in discretechoice,selection and other economic models.Working paper.
    • [20] Frankl, P. y Maehara, H. (1988). The Johnson-Lindenstrauss lemma andthe sphericity of some graphs.J. Combin. Theory Ser. B.,44(3), 355–362.
    • [21] Gershenfeld, N. (2000).The Nature of Mathematical Modeling. Cambridge:Cambridge Univ. Press.[22] Hand, D.J. (2006). Classifier technology...
    • [23] Hannan, E.J. (1970).Multiple Time Series. John Wiley & Sons.[24] Hassairi, A. y Regaieg, O. (2007). On the Tukey depth of an atomicmeasure.Stat....
    • [25] Johnson, W.B. y Lindenstrauss, J. (1984). Extensions of Lipschitz mappingsinto a Hilbert space.Conference in Modern Analysis and Probability;...
    • [26] Kavalieris, L. (2008). Uniform convergence of autocovariances.Statist.Probab. Lett.,78(6), 830–838.
    • [27] Kong, L. y Zuo, Y. (2010). Smooth depth contours characterize theunderlying distribution.J. Multivariate Anal.,101, 2222-2226.
    • [28] Koshevoy, G. A. (2002). The Tukey depth characterizes the atomic measure.J. Multivariate Anal.,83, 360–364.
    • [29] Lavergne, P. y Patilea, V. (2008). Breaking the curse of dimensionality innonparametric testing.Journal of Econometrics,143, 103-122.
    • [30] Li, J., Cuesta-Albertos, J.A. y Liu, R.Y. (2012). DD-Classifier:Nonparametric Classification Procedure Based on DD-plot.Jour....
    • [31] Liu, R.Y. (1990). On a notion of data depth based on random simplices.Ann. Statist.,18, 405–414.
    • [32] Liu, R.Y., Parelius, J.M. y Singh, K. (1999). Multivariate analysis banddata depth: descriptive statistics, graphics and inference.Ann....
    • [33] Liu, R.Y., Serfling,R. y Souvaine, D.L., editors. (2006). Data Depth: RobustMultivariate Analysis, Computational Geometry and Applications.Amer.Math....
    • [34] Liu, R.Y. y Singh, K. (2006). Rank tests for nonparametric description ofdispersion.Amer. Math. Soc. DIMACS Series,72, 17–35.
    • [35] Lobato, I.N. y Velasco, C. (2004). A simple test of normality for time series.Econometric Theory,20(4), 671–689.
    • [36] L ́opez-Pintado, S. y Romo, J. (2006). Depth-based classification forfunctional data.Amer. Math. Soc. DIMACS Series,72, 103–119.
    • [37] L ́opez-Pintado, S. y Romo, J. (2009). On the concept of depth for functionaldata.J. Amer. Statist. Assoc.,104(486), 718–734.
    • [38] Mosler, K. y Hoberg, R. (2006). Data analysis and classification with thezonoid depth.Amer. Math. Soc. DIMACS Series,72, 49–59.
    • [39] Moulines, E. y Choukri, K. (1996). Time-domain procedures for testingthat a stationary time-series is Gaussian.IEEE Trans. Sig. Proc.,44(8),2010–2025.
    • [40] Nieto-Reyes, A. (2010). Statistical Applications of Random Projections.University of Cantabria. Tesis Doctorales en Red (TDR)....
    • [41] Nieto-Reyes, A. y Battey, H. (2016). A Topologically Valid Definition ofDepth for Functional Data.Statistical Science,31, 61-79.
    • [42] Nieto-Reyes, A., Cuesta-Albertos J.A. y Gamboa F. (2014). Arandom-projection based procedure to test if a stationary process...
    • [43] Nieto-Reyes, A. y Cuesta-Albertos J.A. (2015). M. Hubert, P. Rousseeuwand P. Segaert: Multivariate functional outlier detection.StatisticalMethods...
    • [44] Opazo, L., Raddatz, C. y Schmukler S.L. (2009). The long and the shortof emerging market debt.Policy Research Working Paper Series 5056,...
    • [45] Subba Rao, T. y Gabr, M.M. (1980). A test for linearity of stationary timeseries.J. Time Ser. Anal.,1, 145–158.
    • [46] Stein, M. (1999).Interpolation of Spatial Data: Some Theory for Kriging.Springer.
    • [47] Tukey, J.W. (1975). Mathematics and picturing of data.Proc. of ICM,Vancouver,2, 523–531.
    • [48] Vempala, S.S. (2004). The random projection method.Amer. Math. Soc.DIMACS Series,65.
    • [49] Zuo, Y. y Serfling, R. (2000). General notions of statistical depth function.Ann. Statist.,28(2), 461–482.
    • [50] Zuo, Y. (2003). Projection-based depth functions and associated medians.Ann. Statist.,31(5), 1460–1490.
    • [51] Zuo, Y. (2006). Multidimensional trimming based on projection depth.Ann. Statist.,34(5), 2211–2251.
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