Título: Quantification under prior probability shift: the ratio estimator and its extensions
Palestrante: Rafael Izbicki (UFSCar)

Data: 13 de julho de 2020 (segunda-feira)
Hora: 15:00 h

O seminário será realizado no GoogleMeet. Clique AQUI para acessar. 

Abstract: The quantification problem consists of determining the prevalence of a given label in a target population using labels from a sample from the training population. A common assumption in this situation is that of prior probability shift, that is, once the labels are known, the distribution of the features is the same in the training and target populations. In this paper, we derive a new lower bound for the risk of the quantification problem under the prior shift assumption. Complementing this lower bound, we present a new approximately minimax class of estimators, ratio estimators, which generalize several previous proposals in the literature. Using a weaker version of the prior shift assumption, which can be tested, we show that ratio estimators can be used to build confidence intervals for the quantification problem. We also extend the ratio estimator so that it can:(i) incorporate labels from the target population, when they are available and (ii) estimate how the prevalence of positive labels varies according to a function of certain covariates.

Organizadores: Guilherme Ost e Maria Eulalia Vares