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Volume 38 (2009)
- Issue 3 Pages 111-
- Issue 2 Pages 41-
- Issue 1 Pages 1-
Volume 38, Issue 3
Displaying 1-3 of 3 articles from this issue
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Contributed Papers
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Sarpono Dimulyo, Satoshi Aoki2009 Volume 38 Issue 3 Pages 111-129
Published: 2009
Released on J-STAGE: December 20, 2011
JOURNAL OPEN ACCESSThe 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 local 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.View full abstractDownload PDF (1965K) -
Taka-aki Shiraishi2009 Volume 38 Issue 3 Pages 131-150
Published: 2009
Released on J-STAGE: December 20, 2011
JOURNAL OPEN ACCESSWe consider simultaneous confidence intervals for the differences, ratios, odds ratios among propotions in k binomial populations. Although the simultaneous confidence intervals for all the pairwise differences among the propotions are expressed in Hochberg & Tamhane(1987), the intervals sometimes cause the contradiction including -1 or 1. To solve this contradiction, we construct the simultaneous confidence intervals based on logarithm transformations such as logit. Especially, we derive the upper and lower bounds for the asymptotic distribution of the statistic deriving the simultaneous cofidence intervals for all the pairwise differences. By using the inequalities, it is shown that the conservative is small. For multiple comparisons with a control, the procedures based on the Bonferroni inequality is discussed.View full abstractDownload PDF (703K) -
Hirokazu Yanagihara, Isamu Nagai, Kenichi Satoh2009 Volume 38 Issue 3 Pages 151-172
Published: 2009
Released on J-STAGE: December 20, 2011
JOURNAL OPEN ACCESSIn a ridge regression for an univariate linear regression model, it is common that an optimal ridge parameter is determined by minimizing an information criterion, e.g., Mallows' Cp criterion (Mallows (1973, 1995)). Since the solution to the minimization problem of the information criterion is not expressed by a closed form, an additional computational task is required. On the other hand, a generalized ridge regression proposed by Hoerl and Kennard (1970) has multiple ridge parameters, but optimal ridge parameters are obtained by closed forms. In this paper, we extend the generalized ridge regression to a multivariate linear regression case. Then, Cp criterion for optimizing ridge parameters in the multivariate generalized ridge regression is considered as an estimator of a risk function based on the mean square error of prediction. By correcting a bias of the Cp criterion completely, a bias-corrected Cp criterion named by modified Cp (MCp) criterion is proposed. It is analytically proved that the proposed MCp has not only smaller bias but also smaller variance than an existing Cp criterion and is the uniformly minimum variance unbiased estimator of the risk function. We show that the criterion has useful properties by means of numerical experiments.View full abstractDownload PDF (769K)
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