Direction selection in stochastic directional distance functions

Using Directional Distance Functions to Determine Ranking Ranges in Cross-efficiency Evaluations

Directional Distance Functions and Rate-of-Return Regulation

This paper is concerned with formulating directional distance functions assuming that firms operate subject to rate-of-return regulation. To this end, we consider two different contexts. First, we assume that input prices are known, which allows us to extend the rate of return regulated version of Farrell efficiency. Secondly, we assume that input prices are unknown, showing then that a specifi...

Potential Functions for Stochastic Model Selection

We prove performance guarantees for Bayesian learning algorithms, in particular stochastic model selection, with the help of potential functions. Such a potential quantifies the current state of learning in the system, in a way that the expected error in the next step is bounded by the expected decrease of the potential. For Bayesian stochastic model selection, an appropriate potential function...

Linear Functions Preserving Multivariate and Directional Majorization

Let V and W be two real vector spaces and let &sim be a relation on both V and W. A linear function T : V → W is said to be a linear preserver (respectively strong linear preserver) of &sim if Tx &sim Ty whenever x &sim y (respectively Tx &sim Ty if and only if x &sim y). In this paper we characterize all linear functions T : M_{n,m} → M_{n,k} which preserve or strongly preserve multivariate an...

Portfolio selection in stochastic markets with exponential utility functions

We consider the optimal portfolio selection problem in a multiple period setting where the investor maximizes the expected utility of the terminal wealth in a stochastic market. The utility function has an exponential structure and the market states change according to a Markov chain. The states of the market describe the prevailing economic, financial, social and other conditions that affect t...