Parameter Estimation in Linear Regression Models With Stationary Arma(p,q)-Errors Using Automatic Differentiation
The subject of this work is the defence planning of a point target against an air attack. The defence system is decomposed into a number of sectors. A direct method of coordination is used at the upper level, while the sectors use a discrete-time event-based model and the description of uncertainty by multiple scenarios of an attack. The resulting problems are solved using linear programming. A comparison of two coordination strategies for realistic attack scenarios and an analysis of effectiveness...
A method is presented for segmenting one-dimensional signal whose independent segments are modeled as polynomials, and which is corrupted by additive noise. The method is based on sparse modeling, the main part is formulated as a convex optimization problem and is solved by a proximal splitting algorithm. We perform experiments on simulated and real data and show that the method is capable of reliably finding breakpoints in the signal, but requires careful tuning of the regularization parameters...
Este trabajo estudia el problema de la planificación de la producción en sistemas de fabricación multinivel, con un cuello de botella. El problema se ha abordado mediante una aproximación heurística, resolviendo el problema resultante empleando el método primal dual. El trabajo incluye un algoritmo para la selección sucesiva de los precios de los recursos que garanticen una mejora monótona hacia la solución óptima.
Based upon an observation that it is too restrictive to assume a definite correlation of the underlying asset price and its volatility, we use a hybrid model of the constant elasticity of variance and stochastic volatility to study a portfolio optimization problem for pension plans. By using asymptotic analysis, we derive a correction to the optimal strategy for the constant elasticity of variance model and subsequently the fine structure of the corrected optimal strategy is revealed. The result...
Se estudia el problema de decisión (Θ,Δ,ρ) cuando Θ es un intervalo finito de R y el decisor posee información acerca de las probabilidades de una partición de Θ en subintervalos, de la monotonía de las f.d.d. en dichos intervalos y de algunas restricciones sobre los momentos de la distribución y ciertos generalizadores de éstas dentro de este contexto.
Consideramos la conexión que existe entre la información de Kullback y los tests admisibles óptimos en el conjunto de riesgos de Neyman-Pearson, usando para ello el estudio de problemas de programación matemática de tipo infinito. Se obtienen resultados que caracterizan un subconjunto de soluciones Bayes como consecuencia del conocimiento de la información, así como una medida de discriminación entre hipótesis para el conjunto de riesgos.
At universities focused on economy, Operation Research topics are usually included in the study plan, including solving of Linear Programming problems. A universal tool for their algebraic solution is (numerically difficult) Simplex Algorithm, for which it is necessary to know at least the fundamental of Matrix Algebra. To illustrate this method of solving LP problems and to discuss all types of results, it seems to be very convenient to include a chapter about graphic solutions to LP problems....