Abstract Purpose of Review Epidemiologic research is rarely based on a random sample of
a well-defined target population. We used causal directed acyclic graphs to demonstrate the …

Background Multiple imputation by chained equations (MICE) requires specifying a suitable
conditional imputation model for each incomplete variable and then iteratively imputes the …

Missing data has the potential to affect analyses conducted in all fields of scientific study
including healthcare, economics, and the social sciences. Several approaches to unbiased …

In generalized additive models for location, scale and shape (GAMLSS), the response
distribution is not restricted to belong to the exponential family and all the model's …

We study the identification and estimation of statistical functionals of multivariate data
missing nonmonotonically and not-at-random, taking a semiparametric approach …

This article proposes an approach to estimate and make inference on the parameters of
copula link-based survival models. The methodology allows for the margins to be specified …

We propose estimating a model for Loss Given Default (LGD) that allows accounting for
spatial dependence between peer-to-peer (P2P) loans. We suggest the LGD two-stage …

Properly conducted serological survey can help determine infection disease true spread.
This study aims to estimate the seroprevalence of SARS-CoV-2 antibodies in Saint …

Missing not at random (MNAR) data pose key challenges for statistical inference because
the substantive model of interest is typically not identifiable without imposing further (eg …

Bivariate copula regression allows for the flexible combination of two arbitrary, continuous
marginal distributions with regression effects being placed on potentially all parameters of …