Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...
The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume ...
This thesis develops models for three problems of liquidity under asymmetric information.
In the chapter "Disclosures, Rollover Risk, and Debt Runs" I build a model of dynamic debt
runs without perfect information in order to understand the impact of asset ...
We show that the maximum-likelihood (ML) estimate of models derived from Luce’s choice axiom (e.g., the Plackett–Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inf ...
Vulnerability is estimated in terms of the effects that IEMI may induce to the services provided by the facility under study. We estimate the vulnerability by considering three variables: the likelihood, the risk, and the hardness. The different parameters ...
In this thesis we investigate a non parametric approach to speaker diarization for meeting recordings based on an information theoretic framework. The problem is formulated using the Information Bottleneck (IB) principle. Unlike other approaches where the ...
