RT Journal Article
T1 Central limit theorems for directional and linear random variables with applications
A1 García Portugués, Eduardo
A1 Crujeiras Casais, Rosa María
A1 González Manteiga, Wenceslao
K1 Directional data
K1 Goodness-of-fit
K1 Independence test
K1 Kernel density estimation
K1 Limit distribution
AB A central limit theorem for the integrated squared error of the directional-linear kernel density estimator is established. The result enables the construction and analysis of two testing procedures based on squared loss: a nonparametric independence test for directional and linear random variables and a goodness-of-fit test for parametric families of directional-linear densities. Limit distributions for both test statistics, and a consistent bootstrap strategy for the goodness-of-fit test, are developed for the directional-linear case and adapted to the directional-directional setting. Finite sample performance for the goodness-of-fit test is illustrated in a simulation study. This test is also applied to datasets from biology and environmental sciences
PB Academia Sinica, Institute of Statistical Science
SN 1017-0405
YR 2015
FD 2015
LK http://hdl.handle.net/10347/18562
UL http://hdl.handle.net/10347/18562
LA eng
NO García-Portugués, E., Crujeiras, R. M. & González Manteiga, W. (2015). Central limit theorems for directional and linear random variables with applications. Statistica Sinica, 25, 1207-1229. doi: 10.5705/ss.2014.153
DS Minerva
RD 28 abr 2026