In a regression context, when the relevant subset of explanatory variables is uncertain, it is
common to use a data-driven model selection procedure. Classical linear model theory …

In the recent insurance literature, a variety of finite-dimensional parametric models have
been proposed for analyzing the hump-shaped, heavy-tailed, and highly skewed loss data …

In this paper, we consider robust estimation of claim severity models in insurance, when
data are affected by truncation (due to deductibles), censoring (due to policy limits), and …

In the predictive modeling context, the credibility estimator is a point predictor; it is easy to
calculate and avoids the model misspecification risk asymptotically, but it provides no …

The prediction of future insurance claims based on observed risk factors, or covariates, help
the actuary set insurance premiums. Typically, actuaries use parametric regression models …

With the advent of the “big” data era, large-sample properties of a statistical learning method
are becoming more and more important in an actuary's daily work. For a fixed sample size …

Bias resulting from model misspecification is a concern when predicting insurance claims.
Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method …

We give two prediction intervals for Generalized Linear Models that take model selection
uncertainty into account. The first is a straightforward extension of asymptotic normality …

We give two prediction intervals (PI) for Generalized Linear Models that take model selection
uncertainty into account. The first is a straightforward extension of asymptotic normality …

Quantiles of probability distributions play a central role in the definition of risk measures (eg,
value-at-risk, conditional tail expectation) which in turn are used to capture the riskiness of …