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    Wyświetlanie 1-4 z 4
    Tytuł:
    Small Area Prediction under Alternative Model Specifications
    Autorzy:
    Erciulescu, Andreea L.
    Fuller, Wayne A.
    Powiązania:
    https://bibliotekanauki.pl/articles/465723.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    unit level model
    parametric bootstrap
    double bootstrap
    measurement error
    auxiliary information
    Opis:
    Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a logit model. The proposed bootstrap procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples.
    Źródło:
    Statistics in Transition new series; 2016, 17, 1; 9-24
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improved Estimators of Coefficient of Variation in a Finite Population
    Autorzy:
    Archana, V.
    Aruna Rao, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/465691.pdf
    Data publikacji:
    2011
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Model based comparison
    Coefficient of Variation
    Simple Random Sampling Regression estimator
    Mean Square Error
    Confidence interval
    Opis:
    Coefficient of Variation (C.V) is a unitless measure of dispersion. Hence it is widely used in many scientific and social investigations. Although a lot of work has been done concerning C.V in the infinite population models, it has been neglected in the finite populations. Many areas of applications of C.V involves the finite populations like the use in official statistics and economic surveys of the World Bank. This has motivated us to propose six new estimators of the population C.V. In finite population studies regression estimators are widely used and the idea is exploited to propose the new estimators. Three of the proposed estimators are the regression estimators of the C.V for the study variable while the other three estimators makes use of the regression estimators of population mean and variance to estimate the ratio , the population C.V for the study variable. The bias and mean square error (MSE) of these estimators were derived for the simple random sampling design. The performance of these estimators is compared using two real life data sets. The simulation is carried out to compare the estimators in terms of coverage probability and the length of the confidence interval. The small sample comparison indicates that two of the proposed estimators perform better than the sample C.V. The regression estimator using the information on the Population C.V of the auxiliary variable emerges as the best estimator.
    Źródło:
    Statistics in Transition new series; 2011, 12, 2; 357-380
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling sensitive issues on successive waves
    Autorzy:
    Priyanka, Kumari
    Trisandhya, Pidugu
    Powiązania:
    https://bibliotekanauki.pl/articles/1359283.pdf
    Data publikacji:
    2019-04-25
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Sensitive variable
    Successive waves
    Scrambled Response model
    Class of estimators
    Population mean
    Bias
    Mean squared error
    Optimum matching fraction
    Opis:
    This paper addresses the problem of estimation of population mean of sensitive character using non-sensitive auxiliary variable at current wave in two wave successive sampling. A general class of estimator is proposed and studied under randomized and scrambled response model. Many existing estimators have been modified to work for sensitive population mean estimation. The modified estimators became the members of proposed general class of estimators. The detail properties of all the estimators have been discussed. Their behaviour under randomized and scrambled response techniques have been elaborated. Numerical illustrations including simulation have been accompanied to judge the performance of different estimators. Finally suitable recommendations are forwarded.
    Źródło:
    Statistics in Transition new series; 2019, 20, 1; 41-65
    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 1-4 z 4

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