A parametric quantile regression approach for modelling zero-or-one inflated double bounded data
© 2021 Wiley-VCH GmbH..
Over the last decades, the challenges in applied regression have been changing considerably, and full probabilistic modeling rather than predicting just means is crucial in many applications. Motivated by two applications where the response variable is observed on the unit-interval and inflated at zero or one, we propose a parametric quantile regression considering the unit-Weibull distribution. In particular, we are interested in quantifying the influence of covariates on the quantiles of the response variable. The maximum likelihood method is used for parameters estimation. Monte Carlo simulations reveal that the maximum likelihood estimators are nearly unbiased and consistent. Also, we define a residual analysis to assess the goodness of fit.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:63 |
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| Enthalten in: |
Biometrical journal. Biometrische Zeitschrift - 63(2021), 4 vom: 30. Apr., Seite 841-858 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Menezes, André F B [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 15.10.2021 Date Revised 15.10.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/bimj.202000126 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM320214559 |
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| 245 | 1 | 2 | |a A parametric quantile regression approach for modelling zero-or-one inflated double bounded data |
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| 500 | |a Date Revised 15.10.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021 Wiley-VCH GmbH. | ||
| 520 | |a Over the last decades, the challenges in applied regression have been changing considerably, and full probabilistic modeling rather than predicting just means is crucial in many applications. Motivated by two applications where the response variable is observed on the unit-interval and inflated at zero or one, we propose a parametric quantile regression considering the unit-Weibull distribution. In particular, we are interested in quantifying the influence of covariates on the quantiles of the response variable. The maximum likelihood method is used for parameters estimation. Monte Carlo simulations reveal that the maximum likelihood estimators are nearly unbiased and consistent. Also, we define a residual analysis to assess the goodness of fit | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a parametric quantile regression | |
| 650 | 4 | |a proportions | |
| 650 | 4 | |a unit-Weibull distribution | |
| 650 | 4 | |a zero-or-one inflated models | |
| 700 | 1 | |a Mazucheli, Josmar |e verfasserin |4 aut | |
| 700 | 1 | |a Bourguignon, Marcelo |e verfasserin |4 aut | |
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