combines the characteristics of DA and NDA estimators
such that the signal packet used for estimation contains both pilot and data symbols.
Bias and Mean square error of ratio type variance estimator
For pricing, the estimator
has to rely on experience and historical data unlike a contractor's estimator
who has the actual price from the subcontractors and vendors.
In the present article, we have proposed a class of ratio estimators
for the estimation of finite population mean using simple random sampling scheme when there is maximum and minimum values on both the study and the aux iliary variables and their properties are considered up to the first order of approximation.
help in monitoring, controlling, and supervising of transmission and distribution networks.
can also provide improved decision support with a better understanding of group dynamics.
It can be noted that although both estimators
are very close to the theoretical one, the K phase estimator
seems to have an error smaller than the K power estimator
For this estimator
(referred to as the "DS"), volume-predicted mortality (hospitals' expected mortality rate given their volume) and observed hospital-specific mortality are calculated for each hospital, and these two inputs are weighted based on the reliability of the latter measure.
Depending on the above equation, we can derive the MMSE-MSS estimator
We're focusing on compliance, and the Adherence Estimator
allows us to efficiently determine if someone is a low-, medium- or high-risk patient.
Moreover, as different populations have different species-abundance distributions, the estimator
performance should depend also on the species-abundance distribution of the data set (Bunge & Fitzpatrick, 1993; Soberon & Llorente, 1993; Colwell & Coddington, 1994; Walter & Morand, 1998).
The net result of all these studies has been to show that there exist no clear guidelines for the choice of an estimator
for econometric models.
The main objectives of the present study were: a) describe several lag-one autocorrelation estimators
, presenting the expressions for their calculus; b) propose a new estimator
and test it in comparison with the previously developed estimators
in terms of bias and Mean Square Error (hereinafter, MSE); c) estimate the statistical power of the tests associated with the ten estimators
and based on Monte Carlo sampling.
They, however, determined only a set of estimator
values for the [3.
Finally, they use Monte Carlo simulation based on observed data to explore the performance of RD estimators
with a forcing variable which is standardized within schools by choosing a different cutoff in each school.