Nonparametric estimation of mean and dispersion functions in extended generalized linear models.

In this paper the interest is in regression analysis for data that show possibly overdispersion or underdispersion. The starting point for modeling are generalized linear models in which we no longer admit a linear form for the mean regression function, but allow it to be any smooth function of the covariate(s). In view of analyzing overdispersed or underdispersed data, we additionally bring in an unknown dispersion function. The mean regression function and the dispersion function are then estimated using P-splines with difference type of penalty to prevent from overfitting. We discuss two approaches: one based on an extended quasi-likelihood idea and one based on a pseudo-likelihood approach. The choices of smoothing parameters and implementation issues are discussed. The performance of the estimation method is investigated via simulations and its use is illustrated on several data, including continuous data, counts and proportions.Double exponential family; Extended quasi-likelihood; Modeling; Overdispersion; Pseudo likelihood; P-splines; Regression; Variance estimation; Underdispersion;

Robust Estimation of Mean and Dispersion Functions in Extended Generalized Additive Models

Generalized Linear Models are a widely used method to obtain parametric es- timates for the mean function. They have been further extended to allow the re- lationship between the mean function and the covariates to be more flexible via Generalized Additive Models. However the fixed variance structure can in many cases be too restrictive. The Extended Quasi-Likelihood (EQL) framework allows for estimation of both the mean and the dispersion/variance as functions of covari- ates. As for other maximum likelihood methods though, EQL estimates are not resistant to outliers: we need methods to obtain robust estimates for both the mean and the dispersion function. In this paper we obtain functional estimates for the mean and the dispersion that are both robust and smooth. The performance of the proposed method is illustrated via a simulation study and some real data examples.dispersion;generalized additive modelling;mean regression function;quasilikelihood;M-estimation;P-splines;robust estimation

A pricing formula for delayed claims: Appreciating the past to value the future

We consider the valuation of contingent claims with delayed dynamics in a Black \& Scholes complete market model. We find a pricing formula that can be decomposed into terms reflecting the market values of the past and the present, showing how the valuation of future cashflows cannot abstract away from the contribution of the past. As a practical application, we provide an explicit expression for the market value of human capital in a setting with wage rigidity

Robust estimation of mean and dispersion functions in extended generalized additive models.

Generalized Linear Models are a widely used method to obtain parametric estimates for the mean function. They have been further extended to allow the relationship between the mean function and the covariates to be more flexible via Generalized Additive Models. However the fixed variance structure can in many cases be too restrictive. The Extended Quasi-Likelihood (EQL) framework allows for estimation of both the mean and the dispersion/variance as functions of covariates. As for other maximum likelihood methods though, EQL estimates are not resistant to outliers: we need methods to obtain robust estimates for both the mean and the dispersion function. In this paper we obtain functional estimates for the mean and the dispersion that are both robust and smooth. The performance of the proposed method is illustrated via a simulation study and some real data examples.Dispersion; Generalized additive modelling; Mean regression function; M-estimation; P-splines; Robust estimation;

Towards sustainable energy systems. The role of deregulated electricity markets

Durante los últimos 20 años los gobiernos de muchos países han empezado un proceso de liberalización de sectores clave como los de telecomunicaciones, transportes y energía. Tradicionalmente, estos sectores eran considerados monopolios naturales, pero a finales de los setenta los principios bases del modelo de monopolio fueron criticados por muchos economistas. El 19 de diciembre de 1996, la directiva europea 96/92/CE fue el comienzo de un proceso de reforma estructural de enormes dimensiones. La directiva introdujo una nueva tipología de mercado eléctrico europeo basada en la desregulación del sector. El siguiente trabajo, en la primera parte, trata de analizar la reforma del mercado eléctrico mediante un estudio y una crítica de las directivas europeas y del estado de progreso de los países europeos en la satisfacción de los requerimientos de la reforma. La segunda parte es una elucubración del autor sobre los resultados que la reforma puede lograr con respecto al desarrollo sostenible. El propósito del siguiente proyecto es encontrar una respuesta a la siguiente pregunta: “Are the deregulated energy markets suitable to facilitate a development towards sustainable energy systems?

Large scale characterization of artificial drainage networks: new opportinities from high resolution topography .

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Riccardo Prosdocimi Honors Portfolio

Riccardo Prosdocimi\u27s honors portfolio captured in May 2019