Variance Estimation Based on a Superpopulation Model in Two-Stage Sampling
- 1 June 1979
- journal article
- theory and-method
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 74 (366a) , 438-440
- https://doi.org/10.1080/01621459.1979.10482533
Abstract
Estimators for finite population parameters and their variances in two-stage sampling have been developed by using the linear least-squares prediction approach in a recent article by Royall (1976). This article considers a special case of the superpopulation model assumed by Royall and uses a new technique involving linear combinations of the sample observations to estimate the variances of these estimators. An exact confidence interval for the finite population total is calculated for the case in which all clusters have an equal number of elements and an equal number of elements are sampled from each selected cluster.Keywords
This publication has 8 references indexed in Scilit:
- Exact Confidence Intervals for Linear Combinations of Variance Components in Nested ClassificationsJournal of the American Statistical Association, 1978
- The Linear Least-Squares Prediction Approach to Two-Stage SamplingJournal of the American Statistical Association, 1976
- Robust Estimation in Finite Populations II: Stratification on a Size VariableJournal of the American Statistical Association, 1973
- Robust Estimation in Finite Populations IJournal of the American Statistical Association, 1973
- A Biometrics Invited Paper. Topics in Variance Component EstimationBiometrics, 1971
- Estimation in Multi-Stage SurveysJournal of the American Statistical Association, 1969
- A Procedure to Estimate the Population Mean in Random Effects ModelsTechnometrics, 1967
- On Linear Combinations of Several VariancesJournal of the American Statistical Association, 1956