Estimation of Randomisation Mean Square Error in Small Area Estimation
نویسندگان
چکیده
منابع مشابه
Nonnegative mean squared prediction error estimation in small area estimation
Small area estimation has received enormous attention in recent years due to its wide range of application, particularly in policy making decisions. The variance based on direct sample size of small area estimator is unduly large and there is a need of constructing model based estimator with low mean squared prediction error (MSPE). Estimation of MSPE and in particular the bias correction of MS...
متن کاملOn the Estimation of Mean Square Error of Small Area Predictors
Small area estimation has received considerable attention in recent years due to growing demand for reliable small area statistics• The usual survey estimates, based only on the data from a given small area (domain), are likely to be unreliable due to smallness of sample size in the domain. Therefore, alternative estimators which "borrow strength" from other areas have been proposed in the lite...
متن کاملSome New Developments in Small Area Estimation
Small area estimation has received a lot of attention in recent years due to growing demand for reliable small area statistics. Traditional area-specific estimators may not provide adequate precision because sample sizes in small areas are seldom large enough. This makes it necessary to employ indirect estimators based on linking models. Basic area level and unit level models have been extensiv...
متن کاملOptimization of Generalized Mean-Square Error in Noisy Linear Estimation
A class of least squares problems that arises in linear Bayesian estimation is analyzed. The data vector y is given by the model y = P(Hθ + η) +w, where H is a known matrix, while θ, η, and w are uncorrelated random vectors. The goal is to obtain the best estimate for θ from the measured data. Applications of this estimation problem arise in multisensor data fusion problems and in wireless comm...
متن کاملMean Square Estimation
The problem of parameter estimation in linear model is pervasive in signal processing and communication applications. It is often common to restrict attention to linear estimators, which simplifies the implementation as well as the mathematical derivations. The simplest design scenario is when the second order statistics of the parameters to be estimated are known and it is desirable to minimiz...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Statistical Review
سال: 2018
ISSN: 0306-7734,1751-5823
DOI: 10.1111/insr.12289