A Regressão Robusta surge como uma alternativa ao ajuste por mínimos quadrados quando os erros são contaminados por pontos aberrantes ou existe alguma evidência de violação das suposições do modelo. Na regressão clássica existem critérios de seleção de modelos e medidas de diagnóstico que são muito conhecidos. O objetivo deste trabalho é apresentar os principais critérios robustos de seleção de modelos e medidas de detecção de pontos aberrantes, assim como analisar e comparar o desempenho destes de acordo com diferentes cenários para determinar quais deles se ajustam melhor a determinadas situações. Os critérios de validação cruzada usando simulações de Monte Carlo e o Critério de Informação Bayesiano são conhecidos por desenvolver-se de forma adequada na identificação de modelos. Na dissertação confirmou-se este fato e além disso, suas alternativas robustas também destacam-se neste aspecto. A análise de resíduos constitui uma forte ferramenta da análise diagnóstico de um modelo, no trabalho detectou-se que a análise clássica de resíduos sobre o ajuste do modelo de regressão linear robusta, assim como a análise das ponderações das observações, são medidas de detecção de pontos aberrantes eficientes. Foram aplicados os critérios e medidas analisados ao conjunto de dados obtido da Estação Meteorológica do Instituto de Astronomia, Geofísica e Ciências Atmosféricas da Universidade de São Paulo para detectar quais variáveis meteorológicas influem na temperatura mínima diária durante o ano completo, e ajustou-se um modelo que permite identificar os dias associados à entrada de sistemas frontais. / Robust Regression arises as an alternative to least squares method when errors are contaminated by outliers points or there are some evidence of violation of model assumptions. In classical regression there are several criteria for model selection and diagnostic measures that are well known. The objective of this work is to present the main robust criteria of model selection and outliers detection measures, as well as to analyze and compare their performance according to different stages to determine which of them fit better in certain situations. The cross-validation criteria using Monte Carlo simulations and Beyesian Information Criterion are known to be adequately developed in model identification. This fact was confirmed, and in addition, its robust alternatives also stand out in this aspect. The residual analysis is a strong tool for model diagnostic analysis, in this work it was detected that the classic residual analysis on the robust linear model regression fit, as well as the analysis of the observations weights, are efficient measures of outliers detection points. The analyzed criteria and measures were applied to the data set obtained from the Meteorological Station of the Astronomy, Geophysics and Atmospheric Sciences Institute of São Paulo University to detect which meteorological variables influence the daily minimum temperature during the whole year, and was fitted a model that allows identify the days associated with the entry of frontal systems.

Parmi tous les leviers à disposition des entreprises, la prise en compte de la perception par les clients est aujourd'hui centrale, dès la conception des produits. En effet, le consommateur est aujourd'hui mieux informé et attentif à ce qu'il perçoit de la qualité d'un produit et cette perception lui permet d'établir une valeur d'estime de la qualité esthétique des produits, mais aussi de ses fonctionnalités techniques. La méthodologie de l'analyse de la qualité d'aspect des surfaces est donc un enjeu essentiel pour l'industrie. Deux approches de la fonctionnalité des surfaces sont proposées afin de formaliser la méthodologie de détection, et d'apporter aux experts des critères objectifs d'évaluation des anomalies. La première approche proposée est basée sur la métrologie des surfaces. Elle consiste à analyser les topographies mesurées pour lier la fonction aspect aux caractéristiques géométriques extraites. Une approche multi-échelle basée sur la Décomposition Modale Discrète est mise en oeuvre afin de séparer efficacement les différents ordres de variations géométriques d'une surface, et ainsi d'isoler les anomalies d'aspect. D'autre part, cette méthode permet la mise en oeuvre du calcul des courbures sur une surface de façon simplifiée et robuste. On montre que cet attribut géométrique apporte une information supplémentaire et pertinente en lien avec la fonction aspect. Enfin, ces travaux ont mis en évidence l'importance de la qualité des données sources pour analyser l'aspect, et particulièrement deux difficultés d'ordre métrologiques, liées à la présence de points aberrants (hautes fréquences) et de variations géométriques non intrinsèques aux surfaces, générées par le moyen de mesure (basses fréquences). Une méthode innovante d'identification des points aberrants dédiée à la métrologie des surfaces et basée sur une approche statistique multi-échelle est proposée. La problématique des variations géométriques liées aux tables de positionnement du moyen de mesure est traitée au moyen de la Décomposition Modale, et un protocole pour corriger ces variations est présenté. La seconde approche, plus globale, est basée sur l'interaction entre les surfaces et l'environnement lumineux. L'objet de cette approche de l'analyse de l'aspect est d'apporter une aide aux experts pour mieux détecter les anomalies. Les travaux présentés sont basés sur la technique Polynomial Texture Mappings et consistent à modéliser la réflectance en chaque point des surfaces afin de simuler le rendu visuel sous un éclairage quelconque, à la manière de ce que font les opérateurs en analyse sensorielle pour faciliter la détection. Un dispositif d'aide à l'inspection des surfaces basé sur ce principe est présenté. Enfin, une approche industrielle est proposée afin de montrer comment ces 2 axes de recherche peuvent être complémentaires dans le cadre d'une méthodologie globale, industrielle, de l'analyse de la qualité d'aspect de surfaces. / Accounting for customers' perception of manufactured goods has become a major challenge for the industry. This process is to be established from early design to retail. Customers are nowadays more aware and detail oriented about perceived quality of products. This allows one to set not only an estimated price but also the expected quality of the product. Surface appearance analysis has therefore become a key industrial issue. Two approaches are proposed here to formalize the detection methodology and provide objective criteria for experts to evaluate surface anomalies. The first proposed approach is based on surface metrology. It consists in analyzing the measured topologies in order to bind aspect to geometric characteristics. A multi-scale procedure based on Discrete Modal Decomposition is implemented and allows an effective separation of geometric variations. Accordingly, appearance anomalies can be isolated from other geometrical features. This method enables the calculation of surface curvatures in a simplified and robust manner. It is shown that such geometric information is relevant and bound to visual aspect. The presented work also emphasizes the influence of raw data in aspect analysis. Two main metrological difficulties are investigated: the presence of outliers (High frequencies) and the presence of non surface-related geometric defects, generated by the measuring device (Low frequencies). An innovative method for identifying outliers in surface metrology is presented. It is based on a multi-scale statistical approach. Finally, the issue of geometrical variation due to positioning tables is also addressed. A calibration protocol based on DMD that intends to correct this phenomenon is proposed. The second proposed approach, more global, is based on the interaction of a surface with its light environment. It aims at providing experts with assistance, specifically during the anomaly detection phase. The presented work uses Polynomial Texture Mapping. This technique consists of calculating the reflectance at each point of the surface and simulating its appearance while the lighting angles vary. A surface Inspection Support Device based on this principle is presented and detailed. Finally, an industrial study is proposed that shows how these two academic approaches can be combined within a global industrial methodology dedicated to surface appearance quality.