Survey sampling and small-area estimation

Abstract

This issue is devoted to survey sampling methods. It carries on a tradition of Mathematical Population Studies, after the issues guest-edited by Malay Ghosh and Tomasz Ża̧dło (2014) and Vera Toepoel and Schonlau (2017). Wright (2001) presented some major moments of the history of survey sampling. He acknowledged the pioneering work of Pierre Simon de Laplace (1878-1912; Gillispie, 1997), who estimated the population size of France in 1802 based on a sample of communes, which were administrative districts. He CONTACT Tomasz Żądło

Sampling Design Contribution to Small Area Estimation Procedure in Forest Inventories

“…Knowing the variable of interest, having defined the small area of interest and having available suitable auxiliary information with existing terrestrial data, the last "two steps" (Figure 1) for an effective "small area estimation strategy" are the sampling design and the selection of proper statistical modelling (estimation design) [3,10,11]. From another perspective, the last steps of design and estimation can be considered inseparable [6]. The research of SAE literature is broader out of the forestry borders, as well as, on sampling designs for SAE purposes.…”

“…"SAE techniques address the situation where the number of samples within a small area is too small to provide reliable estimates for that unit" [5]. A small area characterized by small or even null sample size [6]. In the case of a small area, where direct estimations are not possible and when the sample size cannot be increased, indirect estimators (SAE technique) can be applied, "borrowing strength" from other domains or periods and combining the terrestrial information with the extensive use of auxiliary information such as derived from remotely sensed variables [4,[6][7][8].…”

“…A small area characterized by small or even null sample size [6]. In the case of a small area, where direct estimations are not possible and when the sample size cannot be increased, indirect estimators (SAE technique) can be applied, "borrowing strength" from other domains or periods and combining the terrestrial information with the extensive use of auxiliary information such as derived from remotely sensed variables [4,[6][7][8]. Borrowing strength is the basic idea of SAE, where models are fitted globally and applied locally, albeit with minor modifications [9].…”