The aim is to build classifiers that are robust against specific interventions. These domain-shift interventions are defined in a causal graph, extending the framework of Gong et al (2016). In ...

The conditional variance, skewness, and kurtosis play a central role in time series analysis. These three conditional moments (CMs) are often studied by some parametric models but with two big issues: ...

In the prediction of genetic values and quantitative trait loci (QTLs) mapping via the mixed model method incorporating marker information in animal populations, it is important to model the genetic ...

Abstract: A copula is a function that links univariate marginals to their full multivariate distribution. This paper derives the conditional means and conditional variances for the well-known ...

Conditional variance swaps are similar to standard variance swaps but variance exposure is limited to a predefined range of underlying levels. Conditional variance swaps are similar to standard ...

We write MATLAB codes to run an approximate Bayesian filter and compare its conditional mean and variance with an Extended Kalman filter. For a detailed report please consult the file 'project ...

To understand the role of family background in intergenerational mobility, a large literature has focused on the conditional mean of children's economic outcomes given parent's economic status, while ...

dc.description.abstract To understand the role of family background in intergenerational mobility, a large literature has focused on the conditional mean of children's economic outcomes given parent's ...