- Tytuł:
- On the smoothed parametric estimation of mixing proportion under fixed design regression model
- Autorzy:
-
Ramakrishnaiah, Y. S.
Trivedi, Manish
Satish, Konda - Powiązania:
- https://bibliotekanauki.pl/articles/1359251.pdf
- Data publikacji:
- 2019-04-25
- Wydawca:
- Główny Urząd Statystyczny
- Tematy:
-
mixture of distributions
mixing proportion
smoothed parametric estimation
fixed design regression model
mean square error
optimal band width
strong consistency
asymptotic normality - Opis:
- The present paper revisits an estimator proposed by Boes (1966) - James (1978), herein called BJ estimator, which was constructed for estimating mixing proportion in a mixed model based on independent and identically distributed (i.i.d.) random samples, and also proposes a completely new (smoothed) estimator for mixing proportion based on independent and not identically distributed (non-i.i.d.) random samples. The proposed estimator is nonparametric in true sense based on known “kernel function” as described in the introduction. We investigated the following results of the smoothed estimator under the non-i.i.d. set-up such as (a) its small sample behaviour is compared with the unsmoothed version (BJ estimator) based on their mean square errors by using Monte-Carlo simulation, and established the percentage gain in precision of smoothed estimator over its unsmoothed version measured in terms of their mean square error, (b) its large sample properties such as almost surely (a.s.) convergence and asymptotic normality of these estimators are established in the present work. These results are completely new in the literature not only under the case of i.i.d., but also generalises to non-i.i.d. set-up.
- Źródło:
-
Statistics in Transition new series; 2019, 20, 1; 87-102
1234-7655 - Pojawia się w:
- Statistics in Transition new series
- Dostawca treści:
- Biblioteka Nauki
Artykuł