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    Wyświetlanie 1-3 z 3
    Tytuł:
    Small Area Estimation Under a Mixture Model
    Autorzy:
    Chandra, Hukum
    Bathla, HVL
    Sud, U.C.
    Powiązania:
    https://bibliotekanauki.pl/articles/465788.pdf
    Data publikacji:
    2010
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Linear mixed model
    Small area estimation
    EBLUP
    Zero-inflated data
    mixture model
    Opis:
    Small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). We discuss the SAE for zero-inflated data under a mixture model (Fletcher et al., 2005 and Karlberg, 2000) that account for excess zeros in the data. Our results from simulation studies show that mixture model based approach for SAE works well and produces an efficient set of small area estimates. An application to real survey data from the National Sample Survey Organisation of India demonstrates the satisfactory performance of the approach.
    Źródło:
    Statistics in Transition new series; 2010, 11, 3; 76-89
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Small Area Estimation for Skewed Data in the Presence of Zeroes
    Autorzy:
    Karlberg, Forough
    Powiązania:
    https://bibliotekanauki.pl/articles/466079.pdf
    Data publikacji:
    2015
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    small area estimation
    representative outliers
    zero-valued observations lognormal-logistic mixture model
    Opis:
    Skewed distributions with representative outliers pose a problem in many surveys. Various small area prediction approaches for skewed data based on transformation models have been proposed. However, in certain applications of those predictors, the fact that the survey data also contain a non-negligible number of zero-valued observations is sometimes dealt with rather crudely, for instance by arbitrarily adding a constant to each value (to allow zeroes to be considered as “positive observations, only smaller”, instead of acknowledging their qualitatively different nature). On the other hand, while a lognormal-logistic model has been proposed (to incorporate skewed distributions as well as zeroes), that model does not include any hierarchical aspects, and is therefore not explicitly adapted to small area prediction. In this paper, we consolidate the two approaches by extending one of the already established log-transformation mixed small area prediction models to incorporate a logistic component. This allows for the simultaneous, systematic treatment of domain effects, outliers and zero-valued observations in a single framework. We benchmark the resulting model-based predictors (against relevant alternatives) in applications to simulated data as well as empirical data from the Australian Agricultural and Grazing Industries Survey.
    Źródło:
    Statistics in Transition new series; 2015, 16, 4; 541-562
    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 1-3 z 3

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