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Abstract

The Provincial Institute of Statistics and Censuses (IPEC) of Santa Fe, Argentina, intended to develop a sampling design that would allow inferences for different levels of disaggregation inside the province. This paper attempts to solve the stratification problem of the populated areas of the province which are the primary sampling units (PSU) of the sampling design. The first classification is related to size: Cities (more than 10,000 inhabitants), towns (between 2,000 and 10,000 inhabitants), and villages (below 2,000 inhabitants). Within each group, the objective is to create homogeneous groups with respect to social and demographical variables from the 2010 Census. Therefore, 10 indicators are considered in order to identify critical needs of the populated areas and an index of critical needs is obtained using a Robust Principal Component Analysis (RPCA) that considers the Bootstrap inference through the Fast and Robust Bootstrap (FRB). Consequently, this index of critical needs is calculated for each PSU and then the Geometric Stratification Method (GSM) is applied in each size stratum to create homogeneous groups. It is observed that the populated areas in the north of the province have greater critical needs than those areas in the south of the province.

Keywords

Principal components analysis MM-estimators Fast and Robust Bootstrap Geometric Stratification Method

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Stratification of populated areas using a method of robust principal components

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