Intrahousehold allocation in an unintentional random experiment

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Graphical models and point set matching

Point pattern matching in Euclidean Spaces is one of the fundamental problems in Pattern Recognition, having applications ranging from Computer Vision to Computational Chemistry. Whenever two complex patterns are encoded by two sets of points identifying their key features, their comparison can be seen as a point pattern matching problem. This work proposes a single approach to both exact and inexact point set matching in Euclidean Spaces of arbitrary dimension. In the case of exact matching, it is assured to find an optimal solution. For inexact matching (when noise is involved), experimental results confirm the validity of the approach. We start by regarding point pattern matching as a weighted graph matching problem. We then formulate the weighted graph matching problem as one of Bayesian inference in a probabilistic graphical model. By exploiting the existence of fundamental constraints in patterns embedded in Euclidean Spaces, we prove that for exact point set matching a simple graphical model is equivalent to the full model. It is possible to show that exact probabilistic inference in this simple model has polynomial time complexity with respect to the number of elements in the patterns to be matched. This gives rise to a technique that for exact matching provably finds a global optimum in polynomial time for any dimensionality of the underlying Euclidean Space. Computational experiments comparing this technique with well-known probabilistic relaxation labeling show significant performance improvement for inexact matching. The proposed approach is significantly more robust under augmentation of the sizes of the involved patterns. In the absence of noise, the results are always perfect.

A teoria da eficiência no mercado de capitais: uma revisão da literatura e dos trabalhos empíricos: o modelos de Random walk aplicado ao índice de mercado de ações Bovespa

Durante muitos anos uma controversa questão tem ocupado tanto os discursos acadêmicos quanto os financeiros. O problema a ser resolvido diz respeito à evolução passada dos preços das ações e se tal evolução poderia ser utilizada para prever o comportamento dos preços futuros dessas ações.

Estimation of random coefficients logit demand models: an application to the Brazilian fixed income fund market

Estimation of demand and supply in differentiated products markets is a central issue in Empirical Industrial Organization and has been used to study the effects of taxes, merges, introduction of new goods, market power, among others. Logit and Random Coefficients Logit are examples of demand models used to study these effects. For the supply side it is generally supposed a Nash equilibrium in prices. This work presents a detailed discussion of these models of demand and supply as well as the procedure for estimation. Lastly, is made an application to the Brazilian fixed income fund market.

Portifolio selection with random transaction costs

Transaction costs have a random component in the bid-ask spread. Facing a high bid-ask spread, the consumer has the option to wait for better terms oI' trade, but only by carrying an undesirable portfolio balance. We present the best policy in this case. We pose the control problem and show that the value function is the uni que viscosity solution of the relevant variational inequality. Next, a numerical procedure for the problem is presented.

A Higher order and stable method for the numerical integration of Random Differential Equations

Trabalho apresentado no Congresso Nacional de Matemática Aplicada à Indústria, 18 a 21 de novembro de 2014, Caldas Novas - Goiás