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1	Essays on Semiparametric Model Selection and Model Averaging / セミパラメトリックなモデル選択とモデル平均に関する諸研究 Yoshimura, Arihiro 23 March 2015 (has links) 京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第18763号 / 経博第514号 / 新制\|\|経\|\|273(附属図書館) / 31714 / 京都大学大学院経済学研究科経済学専攻 / (主査)教授西山慶彦, 准教授奥井亮, 講師末石直也 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM Model selection Model averaging Semiparametric models Focused information criterion Mallows' Cp 330
2	Model selection Hildebrand, Annelize 11 1900 (has links) In developing an understanding of real-world problems, researchers develop mathematical and statistical models. Various model selection methods exist which can be used to obtain a mathematical model that best describes the real-world situation in some or other sense. These methods aim to assess the merits of competing models by concentrating on a particular criterion. Each selection method is associated with its own criterion and is named accordingly. The better known ones include Akaike's Information Criterion, Mallows' Cp and cross-validation, to name a few. The value of the criterion is calculated for each model and the model corresponding to the minimum value of the criterion is then selected as the "best" model. / Mathematical Sciences / M. Sc. (Statistics) Model selection Discrepancy measures Criteria Akaike Information Criterion Mallows' Cp Cross-validation R-square Adjusted R-square Mean Square Error 519.5 Mallows' Cp Akaike Information Criterion Mathematical models -- Evaluation
3	Model selection Hildebrand, Annelize 11 1900 (has links) In developing an understanding of real-world problems, researchers develop mathematical and statistical models. Various model selection methods exist which can be used to obtain a mathematical model that best describes the real-world situation in some or other sense. These methods aim to assess the merits of competing models by concentrating on a particular criterion. Each selection method is associated with its own criterion and is named accordingly. The better known ones include Akaike's Information Criterion, Mallows' Cp and cross-validation, to name a few. The value of the criterion is calculated for each model and the model corresponding to the minimum value of the criterion is then selected as the "best" model. / Mathematical Sciences / M. Sc. (Statistics) Model selection Discrepancy measures Criteria Akaike Information Criterion Mallows' Cp Cross-validation R-square Adjusted R-square Mean Square Error 519.5 Mallows' Cp Akaike Information Criterion Mathematical models -- Evaluation
4	Transformation model selection by multiple hypotheses testing Lehmann, Rüdiger 17 October 2016 (has links) (PDF) Transformations between different geodetic reference frames are often performed such that first the transformation parameters are determined from control points. If in the first place we do not know which of the numerous transformation models is appropriate then we can set up a multiple hypotheses test. The paper extends the common method of testing transformation parameters for significance, to the case that also constraints for such parameters are tested. This provides more flexibility when setting up such a test. One can formulate a general model with a maximum number of transformation parameters and specialize it by adding constraints to those parameters, which need to be tested. The proper test statistic in a multiple test is shown to be either the extreme normalized or the extreme studentized Lagrange multiplier. They are shown to perform superior to the more intuitive test statistics derived from misclosures. It is shown how model selection by multiple hypotheses testing relates to the use of information criteria like AICc and Mallows’ Cp, which are based on an information theoretic approach. Nevertheless, whenever comparable, the results of an exemplary computation almost coincide. Koordinatentransformation Hypothesentest Affintransformation Ähnlichkeitstransformation Gauß-Markow-Model mit Nebenbedingungen Normierter Lagrange-Multiplikator Studentisierter Lagrange-Multiplikator Akaike's Informationskriterium Mallows’ Cp Coordinate transformation Hypothesis test Affine transformation Similarity transformation Gauss-Markov model with constraints Normalized Lagrange multiplier Studentized Lagrange multiplier Akaike information criterion Mallows’ Cp ddc:500 rvk:ZI 9070
5	Transformation model selection by multiple hypotheses testing Lehmann, Rüdiger January 2014 (has links) Transformations between different geodetic reference frames are often performed such that first the transformation parameters are determined from control points. If in the first place we do not know which of the numerous transformation models is appropriate then we can set up a multiple hypotheses test. The paper extends the common method of testing transformation parameters for significance, to the case that also constraints for such parameters are tested. This provides more flexibility when setting up such a test. One can formulate a general model with a maximum number of transformation parameters and specialize it by adding constraints to those parameters, which need to be tested. The proper test statistic in a multiple test is shown to be either the extreme normalized or the extreme studentized Lagrange multiplier. They are shown to perform superior to the more intuitive test statistics derived from misclosures. It is shown how model selection by multiple hypotheses testing relates to the use of information criteria like AICc and Mallows’ Cp, which are based on an information theoretic approach. Nevertheless, whenever comparable, the results of an exemplary computation almost coincide. info:eu-repo/classification/ddc/500 ddc:500

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