User menu

No effect tests in regression on functional variable and some applications to spectrometric studies

Bibliographic reference Delsol, Laurent. No effect tests in regression on functional variable and some applications to spectrometric studies. In: Computational Statistics, Vol. 28, no. 4, p. 1775-1811 (2013)
Permanent URL
  1. Alsberg Bjørn K., Representation of spectra by continuous functions, 10.1002/cem.1180070305
  2. Aneiros-Pérez Germán, Vieu Philippe, Semi-functional partial linear regression, 10.1016/j.spl.2005.12.007
  3. Borggaard Claus., Thodberg Hans Henrik., Optimal minimal neural interpretation of spectra, 10.1021/ac00029a018
  4. Bosq Denis, Linear Processes in Function Spaces, ISBN:9780387950525, 10.1007/978-1-4612-1154-9
  5. Burba Florent, Ferraty Frédéric, Vieu Philippe, k-Nearest Neighbour method in functional nonparametric regression, 10.1080/10485250802668909
  6. Cao-Abad R., Rate of Convergence for the Wild Bootstrap in Nonparametric Regression, 10.1214/aos/1176348394
  7. Cardot Hervé, Ferraty Frédéric, Sarda Pascal, Functional linear model, 10.1016/s0167-7152(99)00036-x
  8. Cardot Herve, Ferraty Frederic, Mas Andre, Sarda Pascal, Testing Hypotheses in the Functional Linear Model, 10.1111/1467-9469.00329
  9. Cardot Hervé, Goia Aldo, Sarda Pascal, Testing for No Effect in Functional Linear Regression Models, Some Computational Approaches, 10.1081/sac-120028440
  10. Chen Song Xi, Van Keilegom Ingrid, A goodness-of-fit test for parametric and semi-parametric models in multiresponse regression, 10.3150/09-bej208
  11. Crambes Christophe, Kneip Alois, Sarda Pascal, Smoothing splines estimators for functional linear regression, 10.1214/07-aos563
  12. Cuevas Antonio, Fraiman Ricardo, On the Bootstrap Methodology for Functional Data, COMPSTAT 2004 — Proceedings in Computational Statistics (2004) ISBN:9783790815542 p.127-135, 10.1007/978-3-7908-2656-2_9
  13. Cuevas Antonio, Febrero Manuel, Fraiman Ricardo, On the use of the bootstrap for estimating functions with functional data, 10.1016/j.csda.2005.10.012
  14. Dabo-Niang S, Ferraty F, Vieu P (2006) Mode estimation for functional random variable and its application for curves classification. Far East J Theor Stat 18(1):93–119
  15. Davidian M, Lin X, Wang J-L (2004) Introduction [Emerging issues in longitudinal and functional data analysis]. Stat Sinica 14(3):613–614
  16. Delsol Laurent, Ferraty Frédéric, Vieu Philippe, Structural test in regression on functional variables, 10.1016/j.jmva.2010.10.003
  17. Efron B., Bootstrap Methods: Another Look at the Jackknife, 10.1214/aos/1176344552
  18. de Castro B. Fernández, Guillas S, Manteiga W. González, Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels, 10.1198/004017005000000067
  19. Ferraty F., High-dimensional data: a fascinating statistical challenge, 10.1016/j.jmva.2009.10.012
  20. Ferraty F, Vieu P (2000) Dimension fractale et estimation de la régression dans des espaces vectoriels semi-normés. Compte Rendus de l’Académie des Sciences Paris 330:403–406
  21. Ferraty Frédéric, Vieu Philippe, 10.1007/s001800200126
  22. Ferraty F, Vieu P (2006) Nonparametric modelling for functional data. Springer, New York
  23. FERRÉ LOUIS, VILLA NATHALIE, Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach, 10.1111/j.1467-9469.2006.00496.x
  24. Ferraty Frédéric, Vieu Philippe, Additive prediction and boosting for functional data, 10.1016/j.csda.2008.11.023
  25. Ferraty F, Romain Y (2011) The Oxford handbook of functional data analysis. Oxford University Press, Oxford
  26. Ferraty Frédéric, Laksaci Ali, Vieu Philippe, Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models, 10.1007/s11203-004-3561-3
  27. Ferraty F, Mas A, Vieu P (2007) Advances on nonparametric regression for fonctionnal data. ANZ J Stat 49:267–286
  28. Ferraty Frédéric, Vieu Philippe, Viguier-Pla Sylvie, Factor-based comparison of groups of curves, 10.1016/j.csda.2006.10.001
  29. FERRATY FRÉDÉRIC, KEILEGOM INGRID VAN, VIEU PHILIPPE, On the Validity of the Bootstrap in Non-Parametric Functional Regression : Bootstrap in functional regression, 10.1111/j.1467-9469.2009.00662.x
  30. Ferré L, Yao A-F (2005) Smoothed functional inverse regression. Stat Sinica 15(3):665–683
  31. Gadiaga D, Ignaccolo R (2005) Test of no-effect hypothesis by nonparametric regression. Afr Stat 1(1): 67–76
  32. Manteiga Wenceslao González, Vieu Philippe, Statistics for Functional Data, 10.1016/j.csda.2006.10.017
  33. Gao Jiti, Gijbels Irène, Bandwidth Selection in Nonparametric Kernel Testing, 10.1198/016214508000000968
  34. González-Manteiga Wenceslao, Quintela-del-Rı́o Alejandro, Vieu Philippe, A note on variable selection in nonparametric regression with dependent data, 10.1016/s0167-7152(02)00056-1
  35. González Manteiga W., Martı́nez Miranda M.D., Pérez González A., The choice of smoothing parameter in nonparametric regression through Wild Bootstrap, 10.1016/j.csda.2003.12.007
  36. Hall Peter, Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems, 10.1016/0047-259x(90)90080-2
  37. Hall Peter, On Bootstrap Confidence Intervals in Nonparametric Regression, 10.1214/aos/1176348652
  38. Hall Peter, Hart Jeffrey D., Bootstrap Test for Difference between Means in Nonparametric Regression, 10.1080/01621459.1990.10474974
  39. Hardle W., Marron J. S., Semiparametric Comparison of Regression Curves, 10.1214/aos/1176347493
  40. Hardle W., Mammen E., Comparing Nonparametric Versus Parametric Regression Fits, 10.1214/aos/1176349403
  41. Hernández Noslen, Biscay Rolando J., Talavera Isneri, Support Vector Regression Methods for Functional Data, Lecture Notes in Computer Science ISBN:9783540767244 p.564-573, 10.1007/978-3-540-76725-1_59
  42. James Gareth M, Silverman Bernard W, Functional Adaptive Model Estimation, 10.1198/016214504000001556
  43. Laloë Thomas, A k-nearest neighbor approach for functional regression, 10.1016/j.spl.2007.11.014
  44. Lavergne Pascal, Patilea Valentin, Breaking the curse of dimensionality in nonparametric testing, 10.1016/j.jeconom.2007.08.014
  45. Leardi R (2003) Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks. Elsevier, Amsterdam
  46. Leurgans SE, Moyeed RA, Silverman BW (1993) Canonical correlation analysis when the data are curves. J R Stat Soc Ser B 55(3):725–740
  47. Mammen Enno, Bootstrap and Wild Bootstrap for High Dimensional Linear Models, 10.1214/aos/1176349025
  48. Mas A, Pumo B (2007) Functional linear regression with derivatives (submitted)
  49. Müller Hans-Georg, Stadtmüller Ulrich, Generalized functional linear models, 10.1214/009053604000001156
  50. Ramsay J, Dalzell C (1991) Some tools for functional data analysis. J R Stat Soc B 53:539–572
  51. Ramsay J. O., Silverman B. W., Functional Data Analysis, ISBN:9781475771091, 10.1007/978-1-4757-7107-7
  52. Applied Functional Data Analysis: Methods and Case Studies, ISBN:9780387954141, 10.1007/b98886
  53. Ramsay J, Silverman B (2005) Functional data analysis, 2nd edn. Spinger, New York
  54. Rossi Fabrice, Delannay Nicolas, Conan-Guez Brieuc, Verleysen Michel, Representation of functional data in neural networks, 10.1016/j.neucom.2004.11.012
  55. Stute Winfried, Nonparametric model checks for regression, 10.1214/aos/1031833666
  56. Stute W., Manteiga W. González, Quindimil M. Presedo, Bootstrap Approximations in Model Checks for Regression, 10.1080/01621459.1998.10474096
  57. Valderrama Mariano J., An overview to modelling functional data, 10.1007/s00180-007-0043-2