Results found: 2

Search results

Search:
in the keywords: synthetic estimation

Sort By:

Limit search:

A calibrated synthetic estimator for small area estimation

100%

Iseh M. J., Enang E. I.

Statistics in Transition new series

2021

vol. 22

issue 3

15-30

Synthetic estimators are known to produce estimates of population mean in areas where no sampled data are available, but such estimates are usually highly biased with invalid confidence statements. This paper presents a calibrated synthetic estimator of the population mean which addresses these problematic issues. Two known special cases of this estimator were obtained in the form of combined ratio and combined regression synthetic estimators, using selected tuning parameters under stratified sampling. In result, their biases and variance estimators were derived. The empirical demonstration of the usage involving the proposed calibrated estimators shows that they provide better estimates of the population mean than the existing estimators discussed in this study. In particular, the estimators were examined through simulation under three distributional assumptions, namely the normal, gamma and exponential distributions. The results show that they provide estimates of the mean displaying less relative bias and greater efficiency. Moreover, they prove more consistent than the existing classical synthetic estimator. The further evaluation carried out using the coefficient of variation provides additional confirmation of the calibrated estimator's advantage over the existing ones in relation to small area estimation.

Estimation of mask effectiveness perception for small domains using multiple data sources

100%

Sen A., Lahiri P.

Statistics in Transition new series

2022

vol. 23

issue 1

1-20

Understanding the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting everyone in the globe and mask wearing is one of the few precautions against it. To quantify people's perception of mask effectiveness and to prevent the spread of COVID-19 for small areas, we use Understanding America Study's (UAS) survey data on COVID-19 as our primary data source. Our data analysis shows that direct survey-weighted estimates for small areas could be highly unreliable. In this paper, we develop a synthetic estimation method to estimate proportions of perceived mask effectiveness for small areas using a logistic model that combines information from multiple data sources. We select our working model using an extensive data analysis facilitated by a new variable selection criterion for survey data and benchmarking ratios. We suggest a jackknife method to estimate the variance of our estimator. From our data analysis, it is evident that our proposed synthetic method outperforms the direct survey-weighted estimator with respect to commonly used evaluation measures.

Refine search results

2 Statistics in Transition new series

1 Enang E. I.

1 Iseh M. J.

1 Lahiri P.

1 Sen A.

1 2022

1 2021

Search results

A calibrated synthetic estimator for small area estimation

Estimation of mask effectiveness perception for small domains using multiple data sources