- Author
- Ramakrishnaiah Y.S. (Osmania University in Hyderabad, India), Trivedi Manish (School of Sciences, Indira Gandhi; National Open University, New Delhi, India), Satish Konda (Aurora College in Hyderabad, India)
- Title
- On the Smoothed Parametric Estimation of Mixing Proportion under Fixed Design Regression Model
- Source
- Statistics in Transition, 2019, vol. 20, nr 1, s. 87-102, tab., abeks, bibliogr. s. 99-100
- Keyword
- Metody estymacji, Modele regresji, Metody statystyczne
Estimation methods, Regression models, Statistical methods - Note
- summ.
- Abstract
- 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. (original abstract)
- Accessibility
- The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice - Full text
- Show
- Bibliography
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- Cited by
- ISSN
- 1234-7655
- Language
- eng
- URI / DOI
- http://dx.doi.org/10.21307/stattrans-2019-005