Técnicas experimentais de identificação de sistemas de controle têm despertado interesse na indústria, devido a sua facilidade em se ajustar modelos matemáticos, facilitando a formulação e a resolução de problemas de controle de processos. É proposto o uso de uma técnica experimental de identificação de sistemas, utilizando a estrutura matemática linear ARX. Os parâmetros da estrutura matemática ARX são estimados por meio do algoritmo dos mínimos quadrados recursivo (RLS). A comunicação e a aquisição de dados de redes Fieldbus é feita através do padrão de comunicação OPC. / Experimental system identification techniques are considered interesting by industrial sector due to the simple approach to adjust mathematical models, making it easy the formulation and the solution of process control problems. In this work a computational tool is proposed, the Sintonizador, based on the experimental technic of System Identification, using the linear mathematical structure ARX (Auto-Regressive with eXogenous inputs). The ARX structure parameters are estimated by RLS (Recursive Least Square) algorithm. The data comunication and data aquisition of the fieldbus network has been done through of the OPC comunication standard.

Today a standard procedure to analyze the impact of environmental factors on productive efficiency of a decision making unit is to use a two stage approach, where first one estimates the efficiency and then uses regression techniques to explain the variation of efficiency between different units. It is argued that the abovementioned method may produce doubtful results which may distort the truth data represents. In order to introduce economic intuition and to mitigate the problem of omitted variables we introduce the matching procedure which is to be used before the efficiency analysis. We believe that by having comparable decision making units we implicitly control for the environmental factors at the same time cleaning the sample of outliers. The main goal of the first part of the thesis is to compare a procedure including matching prior to efficiency analysis with straightforward two stage procedure without matching as well as an alternative of conditional efficiency frontier. We conduct our study using a Monte Carlo study with different model specifications and despite the reduced sample which may create some complications in the computational stage we strongly agree with a notion of economic meaningfulness of the newly obtained results. We also compare the results obtained by the new method with ones previously produced by Demchuk and Zelenyuk (2009) who compare efficiencies of Ukrainian regions and find some differences between the two approaches. Second part deals with an empirical study of electricity generating power plants before and after market reform in Texas. We compare private, public and municipal power generators using the method introduced in part one. We find that municipal power plants operate mostly inefficiently, while private and public are very close in their production patterns. The new method allows us to compare decision making units from different groups, which may have different objective schemes and productive incentives. Despite the fact that at a certain point after the reform private generators opted not to provide their data to the regulator we were able to construct tree different data samples comprising two and three groups of generators and analyze their production/efficiency patterns. In the third chapter we propose a semiparametric approach with shape constrains which is consistent with monotonicity and concavity constraints. Penalized splines are used to maintain the shape constrained via nonlinear transformations of spline basis expansions. The large sample properties, an effective algorithm and method of smoothing parameter selection are presented in the paper. Monte Carlo simulations and empirical examples demonstrate the finite sample performance and the usefulness of the proposed method.

The paper is devoted to statistical nonparametric estimation of multivariate distribution density. The influence of data pre-clustering on the estimation accuracy of multimodal density is analysed by means of the Monte-Carlo method.

The paper is devoted to statistical nonparametric estimation of multivariate distribution density. The influence of data pre-clustering on the estimation accuracy of multimodal density is analysed by means of the Monte-Carlo method.

<p>The main goal of this work is to explore the effects of time-varying extreme jump tail dependencies in asset markets. Consequently, a lot of attention has been devoted to understand the extremal tail dependencies between of assets. As pointed by Hansen (2013), the estimation of tail risks dependence is a challenging task and their implications in several sectors of the economy are of great importance. One of the principal challenges is to provide a measure systemic risks that is, in principle, statistically tractable and has an economic meaning. Therefore, there is a need of a standardize dependence measures or at least to provide a methodology that can capture the complexity behind global distress in the economy. These measures should be able to explain not only the dynamics of the most recent financial crisis but also the prior events of distress in the world economy, which is the motivation of this paper. In order to explore the tail dependencies I exploit the information embedded in option prices and intra-daily high frequency data. </p><p>The first chapter, a co-authored work with Andrew Patton, proposes a new class of dynamic copula models for daily asset returns that exploits information from high frequency (intra-daily) data. We augment the generalized autoregressive score (GAS) model of Creal, et al. (2013) with high frequency measures such as realized correlation to obtain a "GRAS" model. We find that the inclusion of realized measures significantly improves the in-sample fit of dynamic copula models across a range of U.S. equity returns. Moreover, we find that out-of-sample density forecasts from our GRAS models are superior to those from simpler models. Finally, we consider a simple portfolio choice problem to illustrate the economic gains from exploiting high frequency data for modeling dynamic dependence.</p><p>In the second chapter using information from option prices I construct two new measures of dependence between assets and industries, the Jump Tail Implied Correlation and the Tail Correlation Risk Premia. The main contribution in this chapter is the construction of a systemic risk factor from daily financial measures using a quantile-regression-based methodology. In this direction, I fill the existing gap between downturns in the financial sector and the real economy. I find that this new index performs well to forecast in-sample and out-of-sample quarterly macroeconomic shocks. In addition, I analyze whether the tail risk of the correlation may be priced. I find that for the S&P500 and its sectors there is an ex ante premium to hedge against systemic risks and changes in the aggregate market correlation. Moreover, I provide evidence that the tails of the implied correlation have remarkable predictive power for future stock market returns.</p> / Dissertation

We consider the problem of estimating the reliability of bundles constructed of several fibres, given a particular kind of censored data. The bundles consist of several fibres which have their own independent identically dis-tributed failure stresses (i.e.the forces that destroy the fibres). The force applied to a bundle is distributed between the fibres in the bundle, accord-ing to a load-sharing model. A bundle with these properties is an example of a load-sharing system. Ropes constructed of twisted threads, compos-ite materials constructed of parallel carbon fibres, and suspension cables constructed of steel wires are all examples of load-sharing systems. In par-ticular, we consider bundles where load-sharing is described by either the Equal load-sharing model or the more general Local load-sharing model. In order to estimate the cumulative distribution function of failure stresses of bundles, we need some observed data. This data is obtained either by testing bundles or by testing individual fibres. In this thesis, we develop several theoretical testing methods for both fibres and bundles, and related methods of statistical inference. Non-parametric and parametric estimators of the cumulative distribu-tion functions of failure stresses of fibres and bundles are obtained from different kinds of observed data. It is proved that most of these estimators are consistent, and that some are strongly consistent estimators. We show that resampling, in this case random sampling with replacement from sta-tistically independent portions of data, can be used to assess the accuracy of these estimators. Several numerical examples illustrate the behavior of the obtained estimators. These examples suggest that the obtained estimators usually perform well when the number of observations is moderate.

The underlying aim of this research is to investigate the mathematical descriptions of homogeneous textures in digital images for the purpose of segmentation and recognition. The research covers the problem of testing these mathematical descriptions by using them to generate synthetic realisations of the homogeneous texture for subjective and analytical comparisons with the source texture from which they were derived. The application of this research is in analysing satellite or airborne images of the Earth's surface. In particular, Synthetic Aperture Radar (SAR) images often exhibit regions of homogeneous texture, which if segmented, could facilitate terrain classification. In this thesis we present noncausal, nonparametric, multiscale, Markov random field (MRF) models for recognising and synthesising texture. The models have the ability to capture the characteristics of, and to synthesise, a wide variety of textures, varying from the highly structured to the stochastic. For texture synthesis, we introduce our own novel multiscale approach incorporating a new concept of local annealing. This allows us to use large neighbourhood systems to model complex natural textures with high order statistical characteristics. The new multiscale texture synthesis algorithm also produces synthetic textures with few, if any, phase discontinuities. The power of our modelling technique is evident in that only a small source image is required to synthesise representative examples of the source texture, even when the texture contains long-range characteristics. We also show how the high-dimensional model of the texture may be modelled with lower dimensional statistics without compromising the integrity of the representation. We then show how these models -- which are able to capture most of the unique characteristics of a texture -- can be for the ``open-ended'' problem of recognising textures embedded in a scene containing previously unseen textures. Whilst this technique was developed for the practical application of recognising different terrain types from Synthetic Aperture Radar (SAR) images, it has applications in other image processing tasks requiring texture recognition.

Técnicas experimentais de identificação de sistemas de controle têm despertado interesse na indústria, devido a sua facilidade em se ajustar modelos matemáticos, facilitando a formulação e a resolução de problemas de controle de processos. É proposto o uso de uma técnica experimental de identificação de sistemas, utilizando a estrutura matemática linear ARX. Os parâmetros da estrutura matemática ARX são estimados por meio do algoritmo dos mínimos quadrados recursivo (RLS). A comunicação e a aquisição de dados de redes Fieldbus é feita através do padrão de comunicação OPC. / Experimental system identification techniques are considered interesting by industrial sector due to the simple approach to adjust mathematical models, making it easy the formulation and the solution of process control problems. In this work a computational tool is proposed, the Sintonizador, based on the experimental technic of System Identification, using the linear mathematical structure ARX (Auto-Regressive with eXogenous inputs). The ARX structure parameters are estimated by RLS (Recursive Least Square) algorithm. The data comunication and data aquisition of the fieldbus network has been done through of the OPC comunication standard.

This paper surveys four distinct approaches to frontier estimation of multi-output (and simultaneously multi-input) technologies, when nothing but noisy quantity data are available. Parametrized distributions for inefficiency and noise are necessary for identification of inefficiency, when only cross-sectional data are available. In other respects suitable techniques may differ widely, as is shown. A final technique presented rigorously exploits the possibilities from panel-data by dropping parametrization of distributions as well as functional forms. It is illustrated how this last technique can be coupled with the others to provide a state-of-the-art estimation procedure for this setting. (author's abstract) / Series: Department of Economics Working Paper Series

Le travail est consacré aux questions de la statistique des processus stochastiques. Particulièrement, on considère deux problèmes d'estimation. Le premier chapitre se concentre sur le problème d'estimation non-paramétrique pour le processus de Poisson non-homogène. On estime la fonction moyenne de ce processus, donc le problème est dans le domaine d'estimation non-paramétrique. On commence par la définition de l'efficacité asymptotique dans les problèmes non-paramétriques et on procède à exploration de l'existence des estimateurs asymptotiquement efficaces. On prend en considération la classe des estimateurs à noyau. Dans la thèse il est démontré que sous les conditions sur les coefficients du noyau par rapport à une base trigonométrique, on a l'efficacité asymptotique dans le sens minimax sur les ensembles divers. Les résultats obtenus soulignent le phénomène qu'en imposant des conditions de régularité sur la fonction inconnue, on peut élargir la classe des estimateurs asymptotiquement efficaces. Pour comparer les estimateurs asymptotiquement efficaces (du premier ordre), on démontre une inégalité qui nous permet de trouver un estimateur qui est asymptotiquement efficace du second ordre. On calcule aussi la vitesse de convergence pour cet estimateur, qui dépend de la régularité de la fonction inconnue et finalement on calcule la valeur minimale de la variance asymptotique pour cet estimateur. Cette valeur joue le même rôle dans l'estimation du second ordre que la constantede Pinsker dans le problème d'estimation de la densité ou encore l'information de Fisher dans les problèmes d'estimation paramétrique.Le deuxième chapitre est dédié au problème de l’estimation de la solution d’une équation différentielle stochastique rétrograde (EDSR). On observe un processus de diffusion qui est donnée par son équation différentielle stochastique dont le coefficient de la diffusion dépend d’un paramètre inconnu. Les observations sont discrètes. Pour estimer la solution de l’EDSR on a besoin d’un estimateur-processus pour leparamètre, qui, chaque instant n’utilise que la partie des observations disponible. Dans la littérature il existe une méthode de construction, qui minimise une fonctionnelle. On ne pouvait pas utiliser cet estimateur, car le calcul serait irréalisable. Dans le travail nous avons proposé un estimateur-processus qui a la forme simple et peut être facilement calculé. Cet estimateur-processus est un estimateur asymptotiquementefficace et en utilisant cet estimateur on estime la solution de l’EDSR de manière efficace aussi. / This work is devoted to the questions of the statistics of stochastic processes. Particularly, the first chapter is devoted to a non-parametric estimation problem for an inhomogeneous Poisson process. The estimation problem is non-parametric due to the fact that we estimate the mean function. We start with the definition of the asymptotic efficiency in non-parametric estimation problems and continue with examination of the existence of asymptotically efficient estimators. We consider a class of kernel-type estimators. In the thesis we prove that under some conditions on the coefficients of the kernel with respect to a trigonometric basis we have asymptotic efficiency in minimax sense over various sets. The obtained results highlight the phenomenon that imposing regularity conditions on the unknown function, we can widen the class ofasymptotically efficient estimators. To compare these (first order) efficient estimators, we prove an inequality which allows us to find an estimator which is asymptotically efficient of second order. We calculate also the rate of convergence of this estimator, which depends on the regularity of the unknown function, and finally the minimal value of the asymptotic variance for this estimator is calculated. This value plays the same role in the second order estimation as the Pinsker constant in the density estimation problem or the Fisher information in parametric estimation problems. The second chapter is dedicated to a problem of estimation of the solution of a Backward Stochastic Differential Equation (BSDE). We observe a diffusion process which is given by its stochastic differential equation with the diffusion coefficientdepending on an unknown parameter. The observations are discrete. To estimate the solution of a BSDE, we need an estimator-process for a parameter, which, for each given time, uses only the available part of observations. In the literature there exists a method of construction, which minimizes a functional. We could not use this estimator, because the calculations would not be feasible. We propose an estimator-process which has a simple form and can be easily computed. Using this estimator we estimate the solution of a BSDE in an asymptotically efficient way.