We follow the model assisted regression estimator approach outlined by [16], to estimate both mean county biomass and also the standard error of the mean.
A limited simulation study shows that the minimum model mean squared error regression estimator performs well compared to the generalized least squares regression estimator, even when the working model is inappropriate.
With the regression estimator (model-assisted) fewer strata led to better estimates for the Green Hills datasets (Tables 8 and 9) but had minimal effect on the RMSE values for the Nundle datasets (Tables 6 and 7).
In addition to the usual design-based estimator, a regression estimator (model-assisted) was also employed using strata and OV as the auxiliary variables.
The regression estimator constructed with model parameters that differ considerably from the true parameters performed well in a Monte Carlo study.
The weighted sums in the form ∑ k ≤ n a n, k X k can play an important role in various applied and theoretical problems, such as those of the least squares estimators (see Kafles and Bhaskara Rao [14]) and M-estimates (see Rao and Zhao [15]) in linear models, the nonparametric regression estimators (see Priestley and Chao [16]) etc.
The weighted sums ∑ k ≤ n a n, k X k can play an important role in various applied and theoretical problems, such as those of the least squares estimators (see Kafles and Bhaskara Rao [5]) and M-estimates (see Rao and Zhao [6]) in linear models, the nonparametric regression estimators (see Priestley and Chao [7]), etc.
More generally, continuity in the density functions and implies that regression discontinuity can be applied to other estimators that do not rely solely on the mean, such as marginal effects (risk or rate differences) in multiplicative models and quantile regression estimators.
For model-assisted estimation, the variance estimator of the so-called generalized regression estimator is [60].
Two types of regression estimator, the simple regression estimator and the ratio estimator are compared with the π* estimator in two case studies.
