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Mir, S. A.
- Variance Estimation Using Linear Combination of Non-Conventional Measures, Quartile Average and Deciles Mean
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Authors
Affiliations
1 Division of Agricultural Statistics, SKUAST, Kashmir (J&K), IN
1 Division of Agricultural Statistics, SKUAST, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 2 (2017), Pagination: 431-434Abstract
Use of auxiliary information in survey sampling plays important role in getting more precision for estimating population parameters. Thus it has now become indispensable to use auxiliary information, thus in this paper we propose new modified ratio estimators by using the auxiliary information of non conventional location measures, non conventional measures of dispersion, quartile average, decile mean and their linear combinations for estimating population variance. The properties associated with proposed estimators are assessed by mean square error, bias and compared with existing estimators. By this comparison we conclude that our proposed estimators are more efficient than the existing estimators. To support the theoretical results, numerical study is provided.Keywords
Simple Random Sampling, Bias, Mean Square Error, Downtown’s Method, Deciles, Efficiency.
References
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- Hierarchical Bayes Small Area Estimation under an Area Level Model with Applications to Horticultural Survey Data
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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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