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?:abstract
  • This thesis contains four contributions which advocate cautious statistical modelling and inference. They achieve it by taking sets of models into account, either directly or indirectly by looking at compatible data situations. Special care is taken to avoid assumptions which are technically convenient, but reduce the uncertainty involved in an unjustified manner. This thesis provides methods for cautious statistical modelling and inference, which are able to exhaust the potential of precise and vague data, motivated by different fields of application, ranging from political science to official statistics. (xsd:string)
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  • (GLES) (xsd:string)
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  • GLES-Bibliography (xsd:string)
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  • 8. Fassung, Januar 2019 (xsd:gyear)
?:dateModified
  • 2018 (xsd:gyear)
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  • 2018 (xsd:gyear)
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?:name
  • Contributions to reasoning on imprecise data: imprecise classification trees, generalized linear regression on microaggregated data and imprecise imputation (xsd:string)
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  • phdthesis (xsd:string)
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  • Bibsonomy (xsd:string)
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  • German Longitudinal Election Study (GLES) (xsd:string)
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  • 2018 (xsd:string)
  • FDZ_Wahlen (xsd:string)
  • GLES (xsd:string)
  • GLES_input2018 (xsd:string)
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  • GLES_pro (xsd:string)
  • GLES_version8 (xsd:string)
  • ZA5700 (xsd:string)
  • checked (xsd:string)
  • phdthesis (xsd:string)
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