Estimation of the distribution function using auxiliary information

Abstract

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Quantile estimation using auxiliary information with applications to soil texture data

“…They pro pose using a weighted average of the two estimators, where the weight is estimated to minimize the asymptotic mean squared error the resulting estimator. Goyeneche (1999) investigates an extension of the CDE called the local residuals estimator. The CDE is constructed under the model that the residuals are homoskedastic or have known vaxi-ance function.…”

“…The imputation procedure can be modified to reflect the fact that auxiliary information is not available for the entire population. The estimator is also modified to allow for non-identical distributions among the residuals of the imputation model as in the locaJ residuals estimator studied by Goyeneche (1999). Sampling weights are incorporated into the weighting scheme to account for unequal selection probabilities.…”

“…The modified imputation scheme assumes that the distribution of the residuals is the same within each imputation class, but allows different distributions of residuals between classes. See Goyeneche (1999) We expand the set of horizon-based calibrated values to a set of inch-based calibrated values. A subscript is added to the calibrated value, so that Ighi = Igh when inch i is contained in horizon k of site g. See Table 3.2 for an example.…”