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Not AvailableThe calibration approach is a popular technique for incorporating auxiliary information for estimation of population parameters in survey sampling. In general, the Calibration Approach assumes the availability of population-level auxiliary information. On the contrary, in large scale surveys, it is often the case that population-level data on auxiliary variable is not available, but it is relatively inexpensive to collect. In the present article, in case of non-availability of population-level relatively inexpensive data on auxiliary variable under two stage sampling, we developed product type calibration estimator of the finite population total using double sampling approach along with the sampling variance and variance estimator. The study variable is assumed to be inversely related with the auxiliary variable. Proposed product type calibration estimator was evaluated through a simulation study which showed that the proposed product type calibration estimator was performing efficiently over traditional Narain-Horvitz-Thompson type expansion estimator as well as product estimator of the finite population total in case of two stage sampling involving two phases at both the stages.Not Availabl

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Not AvailableThe Sustainable Development Goal of Zero Hunger is a bold commitment towards 795 million undernourished people to end all forms of hunger and malnutrition by 2030 (http://www.undp.org/sustainable-development-goals/goal-2-zero-hunger/). India, sharing a quarter of the global hunger burden, has set a comprehensive action against the food insecurity and hunger issue through microscopic identification of food insecure mass followed by decentralized level planning and effective monitoring. Availability of reliable disaggregate level statistics using Small Area Estimation (SAE) approach for measuring the prevalence of food insecurity can be a potential key to the Governmental organization to take consistent steps towards framing strategic plans eyeing zero hunger. A pragmatic approach in SAE is to consider Hierarchical Bayes (HB) framework, which provide an added flexibility of using complex models without concerning much about known design variance or traditional normality assumption. However, this approach does not incorporate the survey weights that are essential for valid inference given the informative samples that are produced by complex survey designs. In this paper, involving survey design information a number of model specifications are discussed in area level HB version to generate reliable and representative district and district by social groupwise estimates of food insecurity incidence for rural areas of the State of Odisha in India by combining the Household Consumer Expenditure Survey 2011-2012 data of National Sample Survey Office and with the Population census 2011. Spatial maps have been produced to observe the inequality in food insecurity distribution among the districts as well as districts cross classified by socio-economic categories. Such maps are definitely useful for policy formulation, fund disbursement purpose and for the Government in taking effective administrative decisions targeting zero hunger.Not Availabl

PROJECT REPORT

Not AvailablePrincipal component (PC) based index accounts for the effect of multicollinearity among the indicator variables through the eigen values and eigen vectors derived from the variance-covariance matrix using maximum likelihood (ML)/ordinary least squares (OLS) methods of estimation. However, these methods of estimation of variance covariance matrix are based on the assumption that sample elements, on which the indicator variables are measured, are independent and identically distributed. In complex survey designs, the independence assumption of units does not hold that leads to erroneous estimation of variance covariance matrix under OLS methods. Therefore, in case of survey data there is a need to develop PC based index using survey weights and auxiliary information which excludes the effect of multicollinearity among the indicator variables as well as accounts for the effect of complex survey designs through which the sample data is collected. Therefore under this study different methods of indices development are proposed which are capable to incorporate the survey weights and auxiliary information available in the data.Not Availabl

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Not AvailableSustainable development goal-1 of the United Nations is to end poverty in all its forms everywhere. The estimates of poverty related parameters obtained from large scale sample survey are often available at large domain level (e.g. state level). But, poverty rates are not uniformly distributed across the regions. The regional variations are masked in such large domain level estimates. However, for monitoring the progress of poverty alleviation programmes aimed at reduction of poverty often require micro or disaggregate level estimates. The traditional survey estimation approaches are not suitable for generating the reliable estimates at this level because of sample size problem. It is the main endeavor of Small Area Estimation (SAE) approach to produce micro level statistics with acceptable precision without incurring any extra cost and utilizing existing survey data. In this study, the Hierarchical Bayes approach of SAE has been applied to generate reliable and representative district level poverty incidence for the State of Odisha in India using the Household Consumer Expenditure Survey 2011–2012 data of National Sample Survey Office and linked with Population Census 2011. The results show the precise performance of model based estimates generated by SAE method to a greater extent than the direct survey estimates. A poverty map has also been produced to observe the spatial inequality in poverty distribution.Not Availabl

Product type Calibration Estimation of Finite Population Total under Two Stage Sampling

Not AvailableThe Calibration Approach proposed by Deville and Särndal (1992) is a popular technique to efficiently use auxiliary information in survey sampling. In this study, calibration estimators of the finite population total have been developed under two stage sampling design along with variance of the estimator and the corresponding estimator of variance. It is assumed that the population level complex auxiliary information is available at the second stage of selection and the study variable is inversely related to the available auxiliary information. The proposed calibration estimators were evaluated through a simulation study and it was found that all the proposed product type calibration estimators perform better than the Horvitz-Thompson estimator as well as usual product estimator of the population total under two stage sampling design

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Not AvailableAdaptive cluster sampling (ACS) technique is usually used for estimation of the abundance of an exclusive, clustered biological population. Commonly, neighbouring units are added to the sample if it satisfies a pre-determined criterion. Use of auxiliary information to increase the precision of estimators is a very general practice. This paper deals with the use of auxiliary information for the development of efficient estimator of finite population mean under ACS design using the well-known Calibration Approach given by Deville and Särndal (1992). The statistical performance of the calibration estimators of population mean under ACS are evaluated through a simulation study with respect to conventional Horvitz Thomson (HT) estimator of population mean which do not utilize the auxiliary information. The results of the simulation study conducted on a rare and clustered population often cited in Smith et al. (1995) show that proposed calibration estimators are more efficient than conventional HT estimator of the population mean under ACS with respect to percentage Relative Bias (%RB) and percentage Relative Root Mean Squared Error (%RRMSE).Not Availabl

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Not AvailableAgriculture plays a vital role in the Indian economy and hence collection and maintenance of Agricultural Statistics assume great importance. During past few agricultural years it was observed that a total number of 1300000 (approx) Crop Cutting Experiments (CCE) were conducted in India every year to find out the crop yield estimates of several major and minor crops conducted under General Crop Estimation Surveys (GCES). Due to shortage of manpower and huge bulk of work day by day the data quality is becoming questionable. To tackle this problem, a pilot study was conducted by ICAR-IASRI, New Delhi sponsored by Directorate of Economics and Statistics (DES), Ministry of Agriculture and Farmers welfare (MoA&FW), Govt. of India to generate district level estimates of major crop yield from a reduced sample size of villages selected from the states. With the reduction in number of villages, the problem of no sample size in some districts were faced during the study where common design based estimates of crop yield cannot be generated. To tackle this problem Aggregate level Small Area Estimation (SAE) was used to tackle this problem. The results obtained from this pilot study in the state of Uttar Pradesh for two major crops i.e. rice and wheat for two seasons i.e. Kharif and Rabi of Agriculture Year 2015-16 and for Paddy in Assam for Kharif of the Agriculture Year (AY) 2015-16 in India were discussed. The yield estimates were compared with the estimates released under GCES for AY 2015-16. It was found that the estimates obtained from reduced sample size of number of CCEs w.r.t. GCES, produced similar estimates with acceptable level of precision.Not Availabl

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Not AvailableThe manual on Sampling Methodology for Energy Audit Survey is prepared for adoption among the centres of ICAR-AICRP on Energy in Agriculture and Agro-based Industries (EAAI) so that results may be compared logically. It is hoped that the content of this manual will be very useful in conducting energy audit survey of the allocated crops for various cooperating centres systematically.ICA

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Not AvailableRatio estimator is widely used survey estimation method for estimating the finite population mean (or total) using auxiliary variable which is linearly related to study variable. However, this method requires the availability of aggregate level population information for auxiliary variable which may not be always available. As a result, in many practical situations, the ratio method of estimation cannot be applied. Alternatively, the double (or two-phase) sampling approach is often applied in such cases. This paper develops the calibration approach based finite population ratio estimator using the double sampling. It is assumed that the ratio of the total of auxiliary variables is available for the first phase sample only. The expression for variance and estimator of the variance of the proposed estimator is also developed. In addition, optimum sample sizes for the first and second phase samples are also suggested for a fixed cost. Monte Carlo simulations based on real population show that the proposed estimator is efficient than the existing alternative.Not Availabl

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