Machine Learning

Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite …

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are …

An important question in many machine learning applications is whether two samples arise from the same generating distribution. …

Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. Besides average coverage, one …

In this post I analyse the covid-19 data from https://www.kaggle.com/einsteindata4u/covid19, which contains information about patients from Albert Einstein’s Hospital, in São Paulo (Brazil). My main assumptions in the following analysis are that:

Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and high power in a so-called likelihood-free inference (LFI) setting; that is, a setting where the likelihood is not explicitly known but one can forward-simulate observable data according to a stochastic model. We present ACORE, a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest. We also present a goodness-of-fit procedure for checking whether the constructed tests and confidence regions are valid.

Stacking methods improve the prediction performance of regression models. A simple way to stack base regressions estimators is by …

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high …

The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target 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. 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.