On Mean Estimation Using a Generalized Class of Chain Type Estimator under Successive Sampling

The present paper comprises a significantly generalized class of chain type estimators to estimate the population means on the current occasion under the framework of successive sampling based on auxiliary information on both the occasion. The proposed generalized class constitutes two renowned chain type classes proposed by Singh and Vishwakarma [1, 2]. As its particular case, an improvement over their notion, with some eased regularity conditions, is proposed by us, which consists of chain type regression estimators additionally to chain type ratio estimators. The construction of the proposed class is fruitful in the sense of constructing the chain type classes of estimators in the realm of successive sampling. In terms of efficiency, we provide a comparative study of the proposed class oversample mean estimator, Cochran's estimator [3], Sukhatme et al. estimator [4] and Singh’s estimator [5]. A numerical illustration is demonstrated in support of the proposed class.

Keywords: Auxiliary information, Generalized class of chain type estimator, Optimum replacement policy, Successive sampling.