2015
Authors
Almeida, JP; Oliveira, JF; Pinto, AA;
Publication
CIM Series in Mathematical Sciences
Abstract
2015
Authors
Alvarez Mozos, M; Ferreira, F; Alonso Meijide, JM; Pinto, AA;
Publication
OPTIMIZATION
Abstract
In this paper, we characterize two power indices introduced in [1] using two different modifications of the monotonicity property first stated by [2]. The sets of properties are easily comparable among them and with previous characterizations of other power indices.
2015
Authors
Bourguignon, J; Jeltsch, R; Pinto, AA; Viana, M;
Publication
CIM Series in Mathematical Sciences
Abstract
2015
Authors
Mousa, AS; Pinheiro, D; Pinto, AA;
Publication
OPERATIONAL RESEARCH: IO 2013 - XVI CONGRESS OF APDIO
Abstract
We consider the problem faced by an economic agent trying to find the optimal strategies for the joint management of her consumption from a basket of K goods that may become unavailable for consumption from some random time tau(i) onwards, and her investment portfolio in a financial market model comprised of one risk-free security and an arbitrary number of risky securities driven by a multidimensional Brownian motion. We apply previous abstract results on stochastic optimal control problem with multiple random time horizons to obtain a sequence of dynamic programming principles and the corresponding Hamilton-Jacobi-Bellman equations. We then proceed with a numerical study of the value function and corresponding optimal strategies for the problem under consideration in the case of discounted constant relative risk aversion utility functions (CRRA).
2015
Authors
Bourguignon, J; Jeltsch, R; Pinto, AA; Viana, M;
Publication
CIM Series in Mathematical Sciences
Abstract
2015
Authors
Figueiredo, A; Gomes, P;
Publication
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Abstract
We consider n individuals described by p variables, represented by points of the surface of unit hypersphere. We suppose that the individuals are fixed and the set of variables comes from a mixture of bipolar Watson distributions. For the mixture identification, we use EM and dynamic clusters algorithms, which enable us to obtain a partition of the set of variables into clusters of variables.Our aim is to evaluate the clusters obtained in these algorithms, using measures of within-groups variability and between-groups variability and compare these clusters with those obtained in other clustering approaches, by analyzing simulated and real data.
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