Estimation of Finite Population Mean Through a Two- Parameter Ratio- Product-Ratio-Type Exponential Estimator in Systematic Sampling

In this Paper, we suggest a two parameter ratio-product-ratio-type exponential estimator for estimating the finite population mean in systematic sampling. The bias and mean squared error of the suggested estimator are obtained to the first degree of approximation. It has been shown that the proposed estimator is better than the usual unbiased estimator, Swain’s (1964) ratio estimator, Shukla’s (1971) product estimator and Singh et al’s (2011) estimators under some realistic conditions. An empirical study has been under taken to evaluate the performance of the suggested estimator over other existing estimators

A New Exponential Approach for Reducing the Mean Squared Errors of the Estimators of Population Mean Using Conventional and Non-Conventional Location Parameters

Classes of ratio-type estimators t (say) and ratio-type exponential estimators te (say) of the population mean are proposed, and their biases and mean squared errors under large sample approximation are presented. It is the class of ratio-type exponential estimators te provides estimators more efficient than the ratio-type estimators

Estimation of Mean with two-Parameter Ratio-Product-Ratio Estimator in Double Sampling using Ancillary Information under Non-Response

Ratio-product-ratio estimators with two parameters in double sampling under non-response are considered along with their properties. Practical conditions are obtained in which the suggested estimators are more proficient than other existing estimators. An example is given

An alternative to Kim and Warde's mixed randomized response model

When open or direct surveys are about sensitive matters (e.g. gambling habits, addiction to drug and others intoxicants, alcoholism, proneness to tax invasion, induced abortions, drunken driving, history of past involvement in crimes, and homosexuality), non-response bias and response bias become serious problems because people oftentimes do not wish to give correct information. To reduce non-response and response bias, various alternative approaches have been proposed, for example a randomized response survey technique, or a mixed randomized response model using simple random sampling with a replacement sampling scheme that improves the privacy of respondents, proposed by authors Kim and Warde. In this paper we have suggested an alternative to Kim and Warde's mixed randomized response model to estimate the proportion of qualitative sensitive variable under the conditions presented in both the cases of completely truthful reporting and less than completely truthful reporting by the respondents. Properties of the proposed randomized response model have been studied along with recommendations. We have also extended the proposed model to stratified random sampling. Numerical illustrations and graphs are also given in support of the present study

A Proficient Two-Stage Stratified Randomized Response Strategy

A stratified randomized response model based on R. Singh, Singh, Mangat, and Tracy (1995) improved two-stage randomized response strategy is proposed. It has an optimal allocation and large gain in precision. Conditions are obtained under which the proposed model is more efficient than R. Singh et al. (1995) and H. P. Singh and Tarray (2015) models. Numerical illustrations are also given in support of the present study

A family of estimators of population mean using multi-auxiliary variate and post-stratification

This paper suggests a family of estimators of population mean using multiauxiliary variate based on post-stratified sampling and its properties are studied under large sample approximation. Asymptotically optimum estimator in the class is identified alongwith its approximate variance formulae. The proposed class of estimators is also compared with corresponding unstratified class of estimators based on estimated optimum value. At the end, an empirical study has been carried out to support the proposed methodology