E. Strzalkowska-Kominiak, M. Mahdizadeh
Simple random sampling (SRS) is by far the most common method for data acquisition
in scientific research. In many applications, however, auxiliary information on the variable of interest can be accessed easily and at little cost. Ranked set sampling (RSS) allows the experimenter to utilize such information in drawing a more informative sample. Our work deals with estimating a functional mean in RSS. We show that the proposed mean estimator is superior to its counterpart in SRS. We investigate the performance of the two estimators in simulation study and apply our methodology in the context of diffusion tensor imaging.
Palabras clave: Functional data analysis, nonparametric estimation, ranked set sampling
Programado
Estadística no paramétrica: Alta Dimensión
12 de junio de 2025 17:10
Sala de prensa (MR 13)