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Titlebook: Applications in Statistical Computing; From Music Data Anal Nadja Bauer,Katja Ickstadt,Maurizio Vichi Book 2019 Springer Nature Switzerland

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發(fā)表于 2025-3-23 13:37:49 | 只看該作者
Culture and Empire, 1830–1962: An Overviewification scheme of German business cycles from 1974 to 2009, in particular why the start of the Great Recession (GR) is dated so late. Based on quarterly year-to-year rates of change, 85% of LTP periods are classified correctly, which is the lowest rate of the four phases but is not different from
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發(fā)表于 2025-3-23 15:06:09 | 只看該作者
: Myth Conception and Misconceptionsbration requirement is quite at odds with the possibility of some such ordering. However, if the requirements of calibration and identical sets of debtors are relaxed, comparability obtains more easily. Taking default predictions in the credit rating industry as an example, we show for a database of
13#
發(fā)表于 2025-3-23 18:08:11 | 只看該作者
From Autonomous to Imperial Rulemation up to the second moment of the variables. However, in many microeconomic applications, the variables of interest are skewed (typical examples are individual wages, size of the firms, number of employees, etc.); therefore, third moments might provide useful information for the estimation proce
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發(fā)表于 2025-3-24 02:09:43 | 只看該作者
15#
發(fā)表于 2025-3-24 05:59:04 | 只看該作者
https://doi.org/10.1007/978-3-030-25147-5Statistical computing; Data science; Machine learning; Computational statistics; Classification; Clusteri
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發(fā)表于 2025-3-24 09:58:10 | 只看該作者
978-3-030-25146-8Springer Nature Switzerland AG 2019
17#
發(fā)表于 2025-3-24 13:30:26 | 只看該作者
Nadja Bauer,Katja Ickstadt,Maurizio VichiPresents state-of-the-art research at the interface of statistics and computer science.Contributes to two highly relevant research areas: data analysis and big data.Covers applied fields of research s
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發(fā)表于 2025-3-24 17:45:39 | 只看該作者
Studies in Classification, Data Analysis, and Knowledge Organizationhttp://image.papertrans.cn/a/image/159273.jpg
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發(fā)表于 2025-3-24 21:54:42 | 只看該作者
20#
發(fā)表于 2025-3-24 23:20:01 | 只看該作者
https://doi.org/10.1007/978-3-7091-9898-8uces a cosine depth based distribution method which aims at assigning directional data to classes, given that a training set with class labels is already available. A simulation study evaluating the performance of the proposed method is provided.
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