#Intervalo de optimalidad series

1.2 EvaluaciГіn de la bondad del modelo de un factor. 1.1.3 El modelo CAPM como modelo unifactorial. 1.1.2 Exponentially weighted moving average (EWMA) version of the one-factor model. 1.1.1 La diversiвЂ¦caciГіn de riesgo en el modelo de un factor. NovalesД¬ontents 1 Modelos factoriales 1.1 El modelo de un factor. Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i Гndexs.Modelos Factoriales Alfonso Novales Departamento de Economia Cuantitativa Universidad Complutense Septiembre 2014 Preliminary version No citar sin permiso del autor 2014 A. Tampoc s'autoritza la presentaciГі del seu contingut en una finestra o marc aliГЁ a TDX (framing). No s'autoritza la seva reproducciГі o altres formes d'explotaciГі efectuades amb finalitats de lucre ni la seva comunicaciГі pГєblica des d'un lloc aliГЁ al servei TDX. En qualsevol cas, en la utilitzaciГі dels seus continguts caldrГ indicar de forma clara el nom i cognoms de la persona autora i el tГtol de la tesi doctoral. Per altres utilitzacions es requereix l'autoritzaciГі prГЁvia i expressa de la persona autora.

32 del Text RefГіs de la Llei de Propietat Intel Pot ser utilitzada per a consulta o estudi personal, aixГ com en activitats o materials d'investigaciГі i docГЁncia en els termes establerts a l'art. L'accГ©s als continguts d'aquesta tesi doctoral i la seva utilitzaciГі ha de respectar els drets de la persona autora. (authoremail) false (authoremailshow) GuillГ©n GosГЎlbez, Gonzalo (director) JimГ©nez Esteller, Laureano (director) true (authorsendemail)ДЄDVERTIMENT. OptimitzaciГі dinГ mica EstimaciГі de parГ metres OptimitzaciГі global OptimitzaciГі multi-objectiu OptimizaciГіn dinГЎmica EstimaciГіn de parГЎmetros OptimizaciГіn global Dynamic optimization Parameter estimation Global optimization 51 57 In both publications MIDO problems were reformulated to mixed integer nonlinear programming (MINLP) through the use of orthogonal collocation on finite elements where binary variables are used to model the existence of regulatory interactions. In the third publication, we adopted a multi-criteria MIDO which minimize complexity and fit simultaneously using the epsilon constraint method in which one objective is treated as the objective function while the rest are converted to auxiliary constraints. In the second publication we defined a mixed integer dynamic optimization problem (MIDO) which minimize the Akaike information criterion.

#Intervalo de optimalidad series

In the second and third publications we developed a method that is able to identify the regulatory structure and its corresponding kinetic parameters from time series data. The algorithm iterates between these two levels until a termination criterion is satisfied. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. Rise to a nonconvex nonlinear programming (NLP) problem. Our method is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving In the first we developed a deterministic global optimization method based on the outer approximation for parameter estimation in dynamic biological systems.

From the work we derived three publications. In this thesis we present a systematic methodology to characterize dynamic biological systems from time series data. DYNAMIC MATHEMATICAL TOOLS FOR THE IDENTIFICATION OF REGULATORY STRUCTURES AND KINETIC PARAMETERS IN