Tytuł pozycji:
On Misspecification of Spatial Weight Matrix for Small Area Estimation in Longitudinal Analysis
- Tytuł:
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On Misspecification of Spatial Weight Matrix for Small Area Estimation in Longitudinal Analysis
- Autorzy:
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Żądło, Tomasz
- Powiązania:
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https://bibliotekanauki.pl/articles/632767.pdf
- Data publikacji:
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2012-12-01
- Wydawca:
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Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
- Źródło:
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Comparative Economic Research. Central and Eastern Europe; 2012, 15, 4; 305-318
1508-2008
2082-6737
- Język:
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angielski
- Prawa:
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Wszystkie prawa zastrzeżone. Swoboda użytkownika ograniczona do ustawowego zakresu dozwolonego użytku
- Dostawca treści:
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Biblioteka Nauki
-
Przejdź do źródła  Link otwiera się w nowym oknie
The problem of prediction of subpopulation (domain) total is studied as in
Rao (2003). Considerations are based on spatially correlated longitudinal data.
The domain of interest can be defined after sample selection what implies its
random sample size. The special case of the General Linear Mixed Model is
proposed where two random components obey assumptions of spatial and
temporal moving average process respectively. Moreover, it is assumed that the
population may change in time and elements’ affiliations to subpopulation may
change in time as well. The proposed model is a generalization of longitudinal
models studied by e.g. Verbeke, Molenberghs (2000) and Hedeker, Gibbons
(2006). The best linear unbiased predictor (BLUP) is derived. It may be used
even if the sample size in the subpopulation of interest in the period of interest is
zero. In the Monte Carlo simulation study the accuracy of the empirical version
of the BLUP will be studied in the case of correct and incorrect specification of
the spatial weight matrix. Two cases of model misspecification are studied. In
the first case the misspecified spatial weight is used. In the second case
independence of random components is assumed but the variable which is used
to compute elements of spatial weight matrix in the correct case will be used as
auxiliary variable in the model.