A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data (Q268733)
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scientific article; zbMATH DE number 6569578
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data |
scientific article; zbMATH DE number 6569578 |
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A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data (English)
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15 April 2016
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extreme value prediction
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functional kernel regression
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kernel-form error density
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Markov chain Monte Carlo
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0.9154753
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0.9105311
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0.90752023
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0.8973662
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0.86518544
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0.8619194
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0.8576623
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0.8566577
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