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Iqbal Jeelani, M.
- 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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- Cochran, W.G. (1997). Sampling Techniques, 3rd Ed., John Wiley & Sons, Inc., New York, U.S.A.
- Jeelani, M.I. and Maqbool, S. (2013). Modified ratio estimators of population mean using linear combination of coefficient of skewness and quartile deviation. South Pacific J. Nat. & Appl. Sci., 31 (1) : 39-44.
- Murthy, M.N. (1967). Sampling theory and methods, Statistical Publishing Society, Calcutta (W.B.) India
- Prasad, B. (1989). Some improved ratio type estimators of population mean and ratio infinite population sample surveys, Communications in Statistics: Theory & Methods, 18 : 379–392.
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- Singh, D. and Chaudhary, F.S. (1986). Theory and analysis of sample survey designs, New Age International Publisher.
- Singh, H.P. and Tailor, R. (2003). Use of known correlation Coefficient in estimating the finite population means, Statistics Transition, 6(4) : 555-560
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- Singh, H.P. and Tailor, R. (2005). Estimation of finite population mean with known co-efficient of variation of an auxiliary, STATISTICA, anno 65 (3) : 301-313
- Upadhyaya, L.N. and Singh, H.P. (1999). Use of transformed auxiliary variable in estimating the finite population mean. Biometrical J., 41(5) : 627-636
- Yan, Z. and Tian, B. (2010).Ratio method to the mean estimation using co-efficient of skewness of auxiliary variable, ICICA2010, PartII,CCIS106(2010):103–110.
- Hierarchical Bayes Small Area Estimation under an Area Level Model with Applications to Horticultural Survey Data
Abstract Views :168 |
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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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- Berger, J.O. (1985). Statistical decision theory and bayesian analysis. Springer-Verlag, New York, U.S.A.
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- Chambers, R., Chandra, H., Salvali, N. and Tzaidis, N. (2014). Outlier robust small area estimation. J. Royal Statist. Society : Series B, 76 (1) : 47-69.
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- Datta, G.S., Rao, J.N.K. and Smith, D.D. (2005). On measuring the variability of small area estimators under a basic area level model. Biometrika, 92 : 183-196.
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- Validation of two Parameter Function Height Diameter Models
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Authors
Affiliations
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
2 Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Kashmir (J&K), IN
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
2 Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 9, No 2 (2018), Pagination: 331-334Abstract
Eleven nonlinear height diameter models were fitted and developed for Pinus trees based on individual tree height and diameter at breast height data (n=300) collected from block Langate of Kashmir province in India. Fitting of height diameter models using non-linear least square regression showed that all the parameters across all models were significant. In order to test the predictive performance of the models 10- folded cross-validation technique was used in this study. Comparison of AIC, RMSE, ME and Ad-R2 values for the training and validation data showed that most of the non-linear HD models capture the height diameter relationships for Pinus trees. Validation results suggest that Naslund -2 HD model provide the best height predictions in case of Pinus tree.Keywords
Height, Diameter, Cross Validation, Pinus.References
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- Wani, F. J., Sharma, M.K., Rizvi, S.E.H and Jeelani, M.I. (2017). Predictive modelling and validation for estimating fodder yield ofGrewia optiva.Malaysion J.Sci., 36 (2):103-115.
- Wani, F. J., Sharma, M.K., Rizvi, S.E.H and Jeelani, M.I. (2018). A study on cross validation for model selection and Estimation. Internat. J. Agric. Sci., 14(1) : 165-172.
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