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On mse estimators of eblup of domain total under some longitudinal model

100%
Żądło T.
Mathematical Economics
|
2013
|
issue 9 (16)
117-127
EN
Żądło (2012) proposed a certain unit-level longitudinal model which was a special case of the General Linear Mixed Model. Two vectors of random components included in the model obey assumptions of simultaneous spatial autoregressive process (SAR) and temporal first-order autoregressive process (AR(1)) respectively. Moreover, it is assumed that the population can change in time and the population elements can change its domains’ (subpopulations’) affiliation in time. Under the proposed model, Żądło (2012) derived the Empirical Best Linear Unbiased Predictor (EBLUP) of the domain total. What is more (based on the theorem proved by Żądło (2009)), the approximate equation of the mean squared error (MSE) was derived and its estimator based on the Taylor approximation was proposed. The proposed MSE estimator was derived under some assumptions including that the variance-covariance matrix can be decomposed into linear combination of variance components. The assumption was not met under the proposed model. In the paper the jackknife MSE estimator for the derived EBLUP will be proposed based on the results presented by Jiang, Lahiri, Wan (2002). The bias of the jackknife MSE estimator will be compared in the simulation study with the bias of the MSE estimator based on the Taylor approximation.
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Jackknife Forecasts of Time Series

100%
Wywiał J., Żądło T., University of Economics in Katowice D. o. S.
Acta Universitatis Lodziensis. Folia Oeconomica
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2007
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vol. 206
EN
In the paper we present the examples of forecasts of time series with seasonal fluctuations. Based on the jackknife method we estimate variances of seasonal factors and the MSE of prediction. Jackknife method has been introduced by M. Quenouille (1949) and then it has been developed among others by J. Tukey (1958) and J. Shao, D. Tu (1995).
PL
W pracy zaproponowano wykorzystanie metody jackknife do prognozowania szeregów czasowych. Oprócz problemu prognozowania tą metodą, podjęto także problem oceny średniego błędu tak wyznaczanych prognoz. W oparciu o rzeczywiste dane zaprezentowane zostały przykłady prognozowania szeregów czasowych z wahaniami sezonowymi przy wykorzystaniu wersji jackknife metody wskaźników sezonowości. Oprócz wyznaczenia wartości prognozowanej w rozważanym przypadku będzie możliwa ocena wariancji błędu predykcji. Metodę jackknife wprowadził M. Quenouille (1949), a była rozwijana m. in. przez J. Tukey’a (1958) oraz J. Shao i D. Tu (1995).
3
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Estimation of mask effectiveness perception for small domains using multiple data sources

100%
Sen A., Lahiri P.
Statistics in Transition new series
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2022
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vol. 23
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issue 1
1-20
EN
Understanding the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting everyone in the globe and mask wearing is one of the few precautions against it. To quantify people's perception of mask effectiveness and to prevent the spread of COVID-19 for small areas, we use Understanding America Study's (UAS) survey data on COVID-19 as our primary data source. Our data analysis shows that direct survey-weighted estimates for small areas could be highly unreliable. In this paper, we develop a synthetic estimation method to estimate proportions of perceived mask effectiveness for small areas using a logistic model that combines information from multiple data sources. We select our working model using an extensive data analysis facilitated by a new variable selection criterion for survey data and benchmarking ratios. We suggest a jackknife method to estimate the variance of our estimator. From our data analysis, it is evident that our proposed synthetic method outperforms the direct survey-weighted estimator with respect to commonly used evaluation measures.
4
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On the Simulation Study of Jackknife and Bootstrap MSE Estimators of a Domain Mean Predictor for Fay‑Herriot Model

45%
Krzciuk M. K.
Acta Universitatis Lodziensis. Folia Oeconomica
|
2017
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vol. 5
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issue 331
169-183
PL
W artykule rozważany jest problem estymacji błędu średniokwadratowego (MSE) w przypadku predykcji wartości średniej w domenie, w oparciu o model Faya-Herriota. W badaniu symulacyjnym analizowane są własności ośmiu estymatorów MSE, w tym bazujących na metodzie jackknife (Jiang, Lahiri, Wan (2002), Chen, Lahiri (2002, 2003)) oraz parametrycznej metodzie bootstrap (Gonzalez-Manteiga et al. (2008), Buthar, Lahiri (2003)). W modelu Faya-Herriota zakładana jest niezależność składników losowych, a obciążenia estymatorów MSE są małe dla dużej liczby domen. Celem artykułu jest porównanie własności estymatorów MSE przy różnej liczbie domen i błędnej specyfikacji modelu wynikającej z występowania korelacji efektów losowych w badaniu symulacyjnym.
EN
  We consider the problem of the estimation of the mean squared error (MSE) of some domain mean predictor for Fay‑Herriot model. In the simulation study we analyze properties of eight MSE estimators including estimators based on the jackknife method (Jiang, Lahiri, Wan, 2002; Chen, Lahiri, 2002; 2003) and parametric bootstrap (Gonzalez‑Manteiga et al., 2008; Buthar, Lahiri, 2003). In the standard Fay‑Herriot model the independence of random effects is assumed, and the biases of the MSE estimators are small for large number of domains. The aim of the paper is the comparison of the properties of MSE estimators for different number of domains and the misspecification of the model due to the correlation of random effects in the simulation study.  
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