RT Journal Article
T1 Goodness-of-fit tests based on Rao's divergence under sparseness assumptions
A1 Pardo Llorente, María del Carmen
AB In many practical situations the classical (fixed-cells) assumptions to test goodness-of-fit are inappropriate, and we consider an alternative set of assumptions, which we call sparseness assumptions. It is proved that, under general conditions, the proposed family of statistics based on Rao's divergence is asymptotically normal when the sample size n and the number of cells Mn tend to infinity so that n/Mn→ v > 0. This result is extended to contiguous alternatives, and subsequently it is possible to find the asymptotically most efficient member of the family.
PB Elsevier
SN 0096-3003
YR 2002
FD 2002-08
LK https://hdl.handle.net/20.500.14352/57790
UL https://hdl.handle.net/20.500.14352/57790
LA eng
NO This work was supported by grant BMF 2000-0800.
NO BMF
DS Docta Complutense
RD 17 abr 2025