## アブストラクト

Mixed Geographically Weighted Regression-Kriging Model for Small Area
Estimation, 111-129

Sarpono Dimulyo and Satoshi Aoki

英文要旨

The average of household expenditure per capita, which is usually used as a
main indicator of poverty, is commonly explained as a function of some variables
in a global regression framework. In the global regression framework, the
coefficients in the model are assumed to be equal for all national spatial
units. Naturally, however, the average of household expenditure per capita is
not equally distributed across the national territory. In fact, the expenditure
covariates do not have the same influence on per capita expenditure all over a
country or region. The geographically weighted regression (GWR) analysis could
be a solution to capture spatial variations and to solve the spatial
non-stationarity. Because in GWR models, the spatial relationships are modeled
by introducing distance-based weights and the estimates are provided for each
variable k and each geographical region i. We have explored the possibility of
combining census and survey data in order to construct a GWR model for the
average of household expenditure. In view of the predictions, GWR cannot be used
to interpolate to other regions without estimating a local parameter, whereas
the global regression model allows one to make prediction in other geographical
region. To handle this limitation, we propose spatial kriging predictor for
estimation of locally parameter in other regions. By this approach, we
classified all the villages in Jawa Tengah into poor or disadvantaged villages
and non-poor villages. To classify villages into poor or disadvantaged villages
and non-poor villages, we compare the estimates of the average of household
expenditure with the poverty line, which have been defined in another survey by
BPS Statistics Indonesia.

「2009年第38巻 No.3」目次へ
「応用統計学」総目次へ

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