Population mean estimation in stratified sampling with incomplete frames

Abstract

Survey sampling heavily relies on the availability of a robust sampling frame, which is often difficult to obtain, leading to undercoverage bias and misleading results. In the present era, an incomplete sampling frame poses a significant challenge, affecting survey results and findings. Literature offers widely used solutions to this pervasive issue that include multiple framework approach and post-survey adjustments. The predecessor-successor (PS) method proposed by Hansen et al. (1963) provides a way to uniquely identify the units not listed in the sampling frame by linking them to the existing units. Despite its potential to mitigate the bias arising from incomplete sampling frames, the P-S method was not explored extensively until it was later formulated mathematically by Singh (1983), Singh (1989) and others. Building on this legacy and addressing the limitations of simple random sampling in diverse populations, this paper introduces an estimator for the population mean within a stratified sampling framework. The properties of the proposed estimator are discussed and the findings are further supported by an empirical study as well as a simulation study.

Journal of Statistical Research 2026, Vol. 60, No. 1, pp. 79-90.