References
- Aneiros G, Horová I, Hušková M, et al. Functional and high-dimensional statistics and related fields. In: Conference proceedings IWFOS; Springer; 2020. p. 1–4.
- Pepe MS. The statistical evaluation of medical tests for classification and prediction. Oxford University Press; 2003.
- Gonçalves L, Subtil A, Oliveira MR, et al. ROC curve estimation: an overview. Revstat-Stat J. 2014;12(1):1–20.
- Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J Math Psychol. 1975;12(4):387–415. doi: 10.1016/0022-2496(75)90001-2
- Zhou XH, McClish DK, Obuchowski NA. Statistical methods in diagnostic medicine. John Wiley & Sons; 2009.
- Inácio V, González-Manteiga W, Febrero-Bande M, et al. Extending induced ROC methodology to the functional context. Biostatistics. 2012;13(4):594–608. doi: 10.1093/biostatistics/kxs007
- Carvalho VI, Carvalho M, Alonzo TA, et al. Functional covariate-adjusted partial area under the specificity-ROC curve with an application to metabolic syndrome diagnosis. Ann Appl Stat. 2016;10(3):1472–1495.
- Estévez-Pérez G, Vieu P. A new way for ranking functional data with applications in diagnostic test. Comput Stat. 2021;36(1):127–154. doi: 10.1007/s00180-020-01020-z
- Lloyd CJ. Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems. J Am Stat Assoc. 1998;93(444):1356–1364. doi: 10.1080/01621459.1998.10473797
- Lloyd CJ, Yong Z. Kernel estimators of the ROC curve are better than empirical. Stat Probab Lett. 1999;44(3):221–228. doi: 10.1016/S0167-7152(99)00012-7
- Jokiel-Rokita A, Pulit M. Nonparametric estimation of the ROC curve based on smoothed empirical distribution functions. Stat Comput. 2013;23(6):703–712. doi: 10.1007/s11222-012-9340-x
- Chakraborty A, Chaudhuri P. A wilcoxon–Mann–Whitney-type test for infinite-dimensional data. Biometrika. 2015;102(1):239–246. doi: 10.1093/biomet/asu072
- Hsieh F, Turnbull BW. Nonparametric estimation of the receiver operating characteristic curve. Ann Stat. 1996;25:25–40.
- Zou KH, Hall WJ, Shapiro DE. Smooth nonparametric receiver operating characteristic (ROC) curves for continuous diagnostic tests. Stat Med. 1997;16:2143–2156. doi: 10.1002/(ISSN)1097-0258
- Zhou XH, Harezlak J. Comparison of bandwidth selection methods for kernel smoothing of ROC curves. Stat Med. 2002;21(14):2045–2055. doi: 10.1002/sim.v21:14
- Altman N, Leger C. Bandwidth selection for kernel distribution function estimation. J Stat Plan Inference. 1995;46(2):195–214. doi: 10.1016/0378-3758(94)00102-2
- Hall PG, Hyndman RJ. Improved methods for bandwidth selection when estimating ROC curves. Stat Probab Lett. 2003;64(2):181–189. doi: 10.1016/S0167-7152(03)00150-0
- Peng L, Zhou XH. Local linear smoothing of receiver operating characteristic (ROC) curves. J Stat Plan Inference. 2004;118(1-2):129–143. doi: 10.1016/S0378-3758(02)00394-4
- Pulit M. A new method of kernel-smoothing estimation of the ROC curve. Metrika. 2016;79(5):603–634. doi: 10.1007/s00184-015-0569-1
- Ratón ML. Optimal cutoff points for classification in diagnostic studies: new contributions and software development [dissertation]. Universidade de Santiago de Compostela; 2016.
- R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.
- Cuevas A, Febrero M, Fraiman R. Robust estimation and classification for functional data via projection-based depth notions. Comput Stat. 2007;22(3):481–496. doi: 10.1007/s00180-007-0053-0
- Ferraty F, Vieu P. Nonparametric functional data analysis: theory and practice. New York (NY): Springer; 2006.
- Cuevas A, Febrero M, Fraiman R. An ANOVA test for functional data. Comput Stat Data Anal. 2004;47(1):111–122. doi: 10.1016/j.csda.2003.10.021
- Zhang JT, Liang X. One-way anova for functional data via globalizing the pointwise f-test. Scand J Stat. 2014;41(1):51–71. doi: 10.1111/sjos.v41.1
- Singh D, Febbo PG, Ross K, et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell. 2002;1(2):203–209. doi: 10.1016/S1535-6108(02)00030-2
- Lopez-Pintado S, Torrente A, Torrente MA. Package ‘depthtools’. Biostatistics. 2021;11(2):254–264. doi: 10.1093/biostatistics/kxp056
- Torrente A, López-Pintado S, Romo J. Depthtools: an R package for a robust analysis of gene expression data. BMC Bioinform. 2013;14(1):1–11. doi: 10.1186/1471-2105-14-237
- Goldsmith J, Scheipl F, Huang L, et al. Refund: Regression with functional data. R Package Version 01-16. 2016.
- Sørensen H, Goldsmith J, Sangalli LM. An introduction with medical applications to functional data analysis. Stat Med. 2013;32(30):5222–5240. doi: 10.1002/sim.v32.30
- Greven S, Scheipl F. A general framework for functional regression modelling. Stat Model. 2017;17(1-2):1–35. doi: 10.1177/1471082X16681317
- Estévez-Pérez G, Vilar JA. Functional anova starting from discrete data: an application to air quality data. Environ Ecol Stat. 2013;20(3):495–517. doi: 10.1007/s10651-012-0231-2