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pro vyhledávání: '"Franck, Christopher"'
When probability predictions are too cautious for decision making, boldness-recalibration enables responsible emboldening while maintaining the probability of calibration required by the user. We introduce BRcal, an R package implementing boldness-re
Externí odkaz:
http://arxiv.org/abs/2409.13858
Autor:
Franck, Christopher T.
This chapter provides a tutorial that the reader can follow towards analyzing discounting data. Previous chapters have already described the breadth of outcomes associated with discounting (Odum et al. 2020) and other background information (Odum 201
Externí odkaz:
http://arxiv.org/abs/2408.03929
Standard nonlinear regression is commonly used when modeling indifference points due to its ability to closely follow observed data, resulting in a good model fit. However, standard nonlinear regression currently lacks a reasonable distribution-based
Externí odkaz:
http://arxiv.org/abs/2404.18000
Linear modeling is ubiquitous, but performance can suffer when the model is misspecified. We have recently demonstrated that latent groupings in the levels of categorical predictors can complicate inference in a variety of fields including bioinforma
Externí odkaz:
http://arxiv.org/abs/2404.06698
We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs migh
Externí odkaz:
http://arxiv.org/abs/2305.06262
Probability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., spread out enough to be informative for decision making. However, there
Externí odkaz:
http://arxiv.org/abs/2305.03780
Publikováno v:
In Gait & Posture January 2025 115:59-63
Publikováno v:
Ann. Appl. Stat. 15 (4) 2083 - 2100, December 2021
Predicting the outcome of elections, sporting events, entertainment awards, and other competitions has long captured the human imagination. Such prediction is growing in sophistication in these areas, especially in the rapidly growing field of data-d
Externí odkaz:
http://arxiv.org/abs/2001.00878
Akademický článek
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Standard linear modeling approaches make potentially simplistic assumptions regarding the structure of categorical effects that may obfuscate more complex relationships governing data. For example, recent work focused on the two-way unreplicated layo
Externí odkaz:
http://arxiv.org/abs/1903.01035