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Nazir, Nageena
- A New Approach of Ratio Estimation in Sample Surveys
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Authors
Affiliations
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 100-103Abstract
This article deals with the estimation of population mean under simple random sampling using a new form of ratio estimator. The expression for mean square error and bias has been obtained. An efficiency comparison is considered for proposed estimator with the classical ratio, product and exponential ratio estimator. Finally an empirical study is also carried out to judge the performance of proposed estimator.Keywords
Simple Random Sampling, Ratio Estimator, Mean Square Error, Efficiency, AMS Classification: 62D05.
References
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- Hierarchical Bayes Small Area Estimation under an Area Level Model with Applications to Horticultural Survey Data
Abstract Views :172 |
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Authors
Affiliations
1 Division of Agri-Statistics, SKUAST-K, Shalimar (J&K), IN
2 Division of Agri-Statistics, SKUASTK, Shalimar (J&K), IN
1 Division of Agri-Statistics, SKUAST-K, Shalimar (J&K), IN
2 Division of Agri-Statistics, SKUASTK, Shalimar (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 9, No 1 (2018), Pagination: 215-223Abstract
In this paper we studied Bayesian aspect of small area estimation using Area level model. We proposed and evaluated new prior distribution for the area level model, for the variance component rather than uniform prior. The proposed model is implemented using the MCMC method for fully Bayesian inference. Laplace approximation is used to obtain accurate approximations to the posterior moments. We apply the proposed model to the analysis of horticultural data and results from the model are compared with frequestist approach and with Bayesian model of uniform prior in terms of average relative bias, average squared relative bias and average absolute bias. The numerical results obtained highlighted the superiority of using the proposed prior over the uniform prior.Keywords
Small Area Estimation, Area Level Model, Hierarchical Bayes.References
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