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In dieser Arbeit wird die Smallest-Accepted Methode als neue Lepski-Typ Methode für Modellwahl im geordneten Fall eingeführt. In einem ersten Schritt wird die Methode vorgestellt und im Fall von Schätzproblemen mit bekannter Fehlervarianz untersucht. Die Hauptkomponenten der Methode sind ein Akzeptanzkriterium, basierend auf Modellvergleichen für die eine Familie von kritischen Werten mit einem Monte-Carlo-Ansatz kalibriert wird, und die Wahl des kleinsten (in Komplexität) akzeptierten Modells. Die Methode kann auf ein breites Spektrum von Schätzproblemen angewandt werden, wie zum Beispiel Funktionsschätzung, Schätzung eines linearen Funktionals oder Schätzung in inversen Problemen. Es werden allgemeine Orakelungleichungen für die Methode im Fall von probabilistischem Verlust und einer polynomialen Verlustfunktion gezeigt und Anwendungen der Methode in spezifischen Schätzproblemen werden untersucht. In einem zweiten Schritt wird die Methode erweitert auf den Fall einer unbekannten, möglicherweise heteroskedastischen Fehlerstruktur. Die Monte-Carlo-Kalibrierung wird durch eine Bootstrap-basierte Kalibrierung ersetzt. Eine neue Familie kritischer Werte wird eingeführt, die von den (zufälligen) Beobachtungen abhängt. In Folge werden die theoretischen Eigenschaften dieser Bootstrap-basierten Smallest-Accepted Methode untersucht. Es wird gezeigt, dass unter typischen Annahmen unter normalverteilten Fehlern für ein zugrundeliegendes Signal mit Hölder-Stetigkeits-Index s > 1/4 und log(n) (p^2/n) klein, wobei n hier die Anzahl der Beobachtungen und p die maximale Modelldimension bezeichnet, die Anwendung der Bootstrap-Kalibrierung anstelle der Monte-Carlo-Kalibrierung theoretisch gerechtfertigt ist. / In this thesis, the Smallest-Accepted method is presented as a new Lepski-type method for ordered model selection. In a first step, the method is introduced and studied in the case of estimation problems with known noise variance. The main building blocks of the method are a comparison-based acceptance criterion relying on Monte-Carlo calibration of a set of critical values and the choice of the model as the smallest (in complexity) accepted model. The method can be used on a broad range of estimation problems like function estimation, estimation of linear functionals and inverse problems. General oracle results are presented for the method in the case of probabilistic loss and for a polynomial loss function. Applications of the method to specific estimation problems are studied. In a next step, the method is extended to the case of an unknown possibly heteroscedastic noise structure. The Monte-Carlo calibration step is now replaced by a bootstrap-based calibration. A new set of critical values is introduced, which depends on the (random) observations. Theoretical properties of this bootstrap-based Smallest-Accepted method are then studied. It is shown for normal errors under typical assumptions, that the replacement of the Monte-Carlo step by bootstrapping in the Smallest-Accepted method is valid, if the underlying signal is Hölder-continuous with index s > 1/4 and log(n) (p^2/n) is small for a sample size n and a maximal model dimension p.

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There is a worldwide development, better to say a non-development: We teach paper & pencil skills in primary schools almost like we did 30 or 50 or 100 years ago. Till today the primary school teachers spend up to more than 100 hours in the class room to teach and to train old fashioned algorithms though in daily life situations and for business purposes everybody uses a calculator. Why do we waste so much time of our children to teach them things which later on they will not need? We see an emotional dichotomy. Despite the research results from many research projects in many countries there still is the fear that the use of calculators in primary grades will harm mental arithmetic and estimation skills. To explain and to overcome that fear we will reflect the nature of number sense and of paper&pencil skills more carefully. We realize that the development of number sense is an intuitive and unconscious mental process while the ability to get an exact calculation result is trained logically and consciously. To overcome the above dichotomy we must solve the hidden dichotomy number sense versus precise calculation result. We need a new balance. Different types of examples will be given how we can further the development of number sense in a technology dominated curriculum.

There is a worldwide development, better to say a non-development: We teach paper & pencil skills in primary schools almost like we did 30 or 50 or 100 years ago. Till today the primary school teachers spend up to more than 100 hours in the class room to teach and to train old fashioned algorithms though in daily life situations and for business purposes everybody uses a calculator. Why do we waste so much time of our children to teach them things which later on they will not need? We see an emotional dichotomy. Despite the research results from many research projects in many countries there still is the fear that the use of calculators in primary grades will harm mental arithmetic and estimation skills. To explain and to overcome that fear we will reflect the nature of number sense and of paper&pencil skills more carefully. We realize that the development of number sense is an intuitive and unconscious mental process while the ability to get an exact calculation result is trained logically and consciously. To overcome the above dichotomy we must solve the hidden dichotomy number sense versus precise calculation result. We need a new balance. Different types of examples will be given how we can further the development of number sense in a technology dominated curriculum.