The k-Sample problem for truncated and censored data

• Autores: Adrián Lago Balseiro
• Directores de la Tesis: Jacobo de Uña Álvarez (dir. tes.) , Juan Carlos Pardo Fernández (dir. tes.)
• Lectura: En la Universidade de Vigo ( España ) en 2025
• Idioma: inglés
• Tribunal Calificador de la Tesis: Jane-ling Wang (presid.) , Juan Carlos Escanciano Reyero (secret.) , Irène Gijbels (voc.)
• Enlaces
  • Tesis en acceso abierto en: Investigo
• Resumen

  • The two-sample Kolmogorov-Smirnov (KS) statistic is a classical method to compare two populations. In Survival Analysis, the classical KS method is not applicable in general because the samples may suffer from random truncation or random censoring. The main goal of this thesis is to adapt the classic KS test to left truncation. Properties of the proposed test will be studied, theoretically and through simulations. A bootstrap resampling plan will be proposed to approximate the null distribution of the statistic in practice and the goodness of such approximation will be studied as well. Possible extensions of the method will be studied, such as the extension to a k-sample scenario and the possibility to include right censorship or double truncation. A Cramér-von Mises and Anderson-Darling type statistics will be studied as well.