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

The present research aims to provide a novel approach to produce finite-population estimates under nonprobability
samples and multiple sources (i.e. big data). Although such a method might have numerous
applications in diverse areas of the social sciences, this research will concentrate on a specific exercise
that will significantly impact Colombia's most vulnerable population. The study will attempt to provide
confident finite-population estimates of the socio-economic situation of the armed conflict victims in
Colombia. With such information, the Colombian Government will be able to produce policies and
budgets 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 and
inferences obtained in multiple social sciences.
Probability sampling has been a gold standard since the early decades of the 20th century with rapid
developments 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 and
affordable manner. However, this increasing trend has challenged traditional methods for statistical
inference. Many of these modern data-collection methods are not in line with the probability sampling
standards, therefore producing non-probability samples. These samples may induce inference problems
in finite-population estimation. This research proposal focuses on applying Bayesian inference and
hierarchical models as an alternative approach to improve the precision of the estimates and determine
their uncertainty levels under non-probability samples.