RT Journal Article
T1 Estimation and Testing on Independent Not Identically Distributed Observations Based on Rényi’s Pseudodistances
A1 Castilla González, Elena María
A1 Jaenada Malagón, María
A1 Pardo Llorente, Leandro
AB In real life we often deal with independent but not identically distributed observations (i.n.i.d.o), for which the most well-known statistical model is the multiple linear regression model (MLRM) with non-random covariates. While the classical methods are based on the maximum likelihood estimator (MLE), it is well known its lack of robustness to small deviations from the assumed conditions. In this paper, and based on the Rényi’s pseudodistance (RP), we introduce a new family of estimators in case our information about the unknown parameter is given for i.n.i.d.o.. This family of estimators, let us say minimum RP estimators (as they are obtained by minimizing the RP between the assumed distribution and the empirical distribution of the data), contains the MLE as a particular case and can be applied, among others, to the MLRM with non-random covariates. Based on these estimators, we introduce Wald-type tests for testing simple and composite null hypotheses, as an extension of the classical MLE-based Wald test. Influence functions for the estimators and Wald-type tests are also obtained and analysed. Finally, a simulation study is developed in order to asses the performance of the proposed methods and some real-life data are analysed for illustrative purpose.
PB Institute of Electrical and Electronics Engineers
SN 0018-9448
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/72031
UL https://hdl.handle.net/20.500.14352/72031
LA eng
DS Docta Complutense
RD 8 may 2024