Power logit regression for modeling bounded data
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866918500277157888 |
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| author | Queiroz, Francisco Felipe Ferrari, Silvia Lopes Paula |
| author_facet | Queiroz, Francisco Felipe Ferrari, Silvia Lopes Paula |
| contents | The main purpose of this paper is to introduce a new class of regression models for bounded continuous data, commonly encountered in applied research. The models, named the power logit regression models, assume that the response variable follows a distribution in a wide, flexible class of distributions with three parameters, namely the median, a dispersion parameter and a skewness parameter. The paper offers a comprehensive set of tools for likelihood inference and diagnostic analysis, and introduces the new R package PLreg. Applications with real and simulated data show the merits of the proposed models, the statistical tools, and the computational package. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_01697 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Power logit regression for modeling bounded data Queiroz, Francisco Felipe Ferrari, Silvia Lopes Paula Methodology The main purpose of this paper is to introduce a new class of regression models for bounded continuous data, commonly encountered in applied research. The models, named the power logit regression models, assume that the response variable follows a distribution in a wide, flexible class of distributions with three parameters, namely the median, a dispersion parameter and a skewness parameter. The paper offers a comprehensive set of tools for likelihood inference and diagnostic analysis, and introduces the new R package PLreg. Applications with real and simulated data show the merits of the proposed models, the statistical tools, and the computational package. |
| title | Power logit regression for modeling bounded data |
| topic | Methodology |
| url | https://arxiv.org/abs/2202.01697 |