Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manu...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2024-12-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/5244 |
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| author | Lucas Kook Philipp F. M. Baumann Oliver Dürr Beate Sick David Rügamer |
| author_facet | Lucas Kook Philipp F. M. Baumann Oliver Dürr Beate Sick David Rügamer |
| author_sort | Lucas Kook |
| collection | DOAJ |
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Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow back end with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data.
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| format | Article |
| id | doaj-art-5fa5abff0f8d466d87ad9944cf93643a |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-5fa5abff0f8d466d87ad9944cf93643a2024-12-29T00:12:39ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-12-01111110.18637/jss.v111.i10Estimating Conditional Distributions with Neural Networks Using R Package deeptrafoLucas Kook0Philipp F. M. Baumann1Oliver Dürr2Beate Sick3David Rügamer4Vienna University of Economics and BusinessETH ZurichHTWG KonstanzUniversity of ZurichLMU Munich Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow back end with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data. https://www.jstatsoft.org/index.php/jss/article/view/5244 |
| spellingShingle | Lucas Kook Philipp F. M. Baumann Oliver Dürr Beate Sick David Rügamer Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo Journal of Statistical Software |
| title | Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo |
| title_full | Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo |
| title_fullStr | Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo |
| title_full_unstemmed | Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo |
| title_short | Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo |
| title_sort | estimating conditional distributions with neural networks using r package deeptrafo |
| url | https://www.jstatsoft.org/index.php/jss/article/view/5244 |
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