Modeling Website Visits

TL;DR

This paper introduces a multivariate statistical model for website visits, capturing user behavior with a censored multivariate normal distribution and discrete Pareto IV marginals, incorporating covariates like age and gender.

Contribution

It presents a novel multivariate model that combines censored normal distributions with Pareto IV marginals and enforces sparsity for better interpretability of dependencies.

Findings

Model accurately reflects website visit behavior.

Enables visualization of dependence structure as a graph.

Incorporates covariates such as age and gender.

Abstract

We propose a multivariate model for the number of hits on a set of popular websites, and show it to accurately reflect the behavior recorded in a data set of Internet users in the United States. We assume that the random vector of visits is distributed according to a censored multivariate normal with marginals transformed to be discrete Pareto IV and, following the ideas of Gaussian graphical models, we enforce sparsity on the inverse covariance matrix to reduce dimensionality and to visualize the dependence structure as a graph. The model allows for an easy inclusion of covariates and is useful for comprehending the behavior of Internet users as a function of their age and gender.