Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 3

first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

Discussion of “Probability vs. Nonprobability Sampling: From the Birth of Survey Sampling to the Present Day” by Graham Kalton

100%
Gershunskaya J., Lahiri P.
Statistics in Transition new series
|
2023
|
vol. 24
|
issue 3
31-37
XX
-
2
Publication available in full text mode
Content available

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
EN
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.
3
Publication available in full text mode
Content available

Spatial Prediction in Small Area Estimation

81%
Vogt M., Lahiri P., Münnich R.
Statistics in Transition new series
|
2023
|
vol. 24
|
issue 3
77-94
EN
Small area estimation methods have become a widely used tool to provide accurate estimates for regional indicators such as poverty measures. Recent research has provided evidence that spatial modelling still can improve the precision of regional and local estimates. In this paper, we provide an intrinsic spatial autocorrelation model and prove the propriety of the posterior under a flat p rior. F urther, we show using the SAIPE poverty data that the gain in efficiency using a spatial model can be essentially important in the presence of a lack of strong auxiliary variables.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.