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Wyszukujesz frazę "optimal estimation" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Optimal allocation for equal probability two-stage design
Autorzy:
Molefe, Wilford
Powiązania:
https://bibliotekanauki.pl/articles/2156993.pdf
Data publikacji:
2022-12-15
Wydawca:
Główny Urząd Statystyczny
Tematy:
sample designs
optimal allocation
composite estimation
mean squared error
two-stage sampling
simple random sampling without replacement
Opis:
This paper develops optimal designs when it is not feasible for every cluster to be represented in a sample as in stratified design, by assuming equal probability two-stage sampling where clusters are small areas. The paper develops allocation methods for two-stage sample surveys where small-area estimates are a priority. We seek efficient allocations where the aim is to minimize the linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. We suggest some alternative allocations with a view to minimizing the same objective. Several alternatives, including the area-only stratified design, are found to perform nearly as well as the optimal allocation but with better practical properties. Designs are evaluated numerically using Switzerland canton data as well as Botswana administrative districts data.
Źródło:
Statistics in Transition new series; 2022, 23, 4; 129-148
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
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
    Wyświetlanie 1-2 z 2

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