Statistical inference in the big data era: using hierarchical models to estimate the socio-economic situation of Colombia's armed conflict victims wit

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The present research aims to provide a novel approach to produce finite-population estimates under nonprobabilitysamples and multiple sources (i.e. big data). Although such a method might have numerousapplications in diverse areas of the social sciences, this research will concentrate on a specific exercisethat will significantly impact Colombia's most vulnerable population. The study will attempt to provideconfident finite-population estimates of the socio-economic situation of the armed conflict victims inColombia. With such information, the Colombian Government will be able to produce policies andbudgets with sufficient detail and impact to improve the lives of nearly 20% of the total population.Furthermore, such a methodology will contribute to the statistical and demographic sciences andinferences obtained in multiple social sciences.Probability sampling has been a gold standard since the early decades of the 20th century with rapiddevelopments of design-based techniques. At the same time, "big data" has allowed researchers,governments, and companies, among others, to access large amounts of data in a faster, easier andaffordable manner. However, this increasing trend has challenged traditional methods for statisticalinference. Many of these modern data-collection methods are not in line with the probability samplingstandards, therefore producing non-probability samples. These samples may induce inference problemsin finite-population estimation. This research proposal focuses on applying Bayesian inference andhierarchical models as an alternative approach to improve the precision of the estimates and determinetheir uncertainty levels under non-probability samples.