This article proposes a framework for determining credibility premiums for multiple coverages in a compound risk model with Tweedie distribution. The framework builds upon previous results on credibility premium and provides an explicit multivariate credibility premium formula that is applicable...

Commons problems present behavioral dilemmas, with tensions between individual and collective rationality. When users of a common-pool resource are not effectively excluded, the collective behavior of individuals pursuing their self-interests dissipates resource rents. We derive a non-parametric...

This work reviews the literature on spline local basis methods for non-parametric density estimation. Particular attention is paid to B-spline density estimators which have experienced recent advances in both theory and methodology. These estimators occupy a very interesting space in statistics,...

The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time...

We study nonparametric regression in a setting where N(N-1) dyadic outcomes are observed for N randomly sampled units. Outcomes across dyads sharing a unit in common may be dependent (i.e., our dataset exhibits dyadic dependence). We present two sets of results. First, we calculate lower bounds...

Mixed models are classical tools in statistics for modeling repeated data on subjects, such as data on patients, customers or firms collected over time. These models extend conventional linear models to include latent parameters, called random effects, that capture between-subject variation and...

Personalized or contextual pricing is widespread practice in a number of revenue management problems. A pricing algorithm or platform utilizes a user’s personal data to make the most profitable pricing decisions that could vary among individuals. In this paper, we study the question of...

In this paper, we propose a localized neural network (LNN) model and then develop the LNN based estimation and inferential procedures for dependent data in both cases with quantitative/qualitative outcomes. We explore the use of identification restrictions from a nonparametric regression...