scholarly journals Response Transformation and Profit Decomposition for Revenue Uplift Modeling

2019 ◽  
Author(s):  
Robin M. Gubela ◽  
Stefan Lessmann ◽  
Szymon Jaroszewicz
2020 ◽  
Vol 283 (2) ◽  
pp. 647-661 ◽  
Author(s):  
Robin M. Gubela ◽  
Stefan Lessmann ◽  
Szymon Jaroszewicz

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.


2021 ◽  
Vol 99 ◽  
pp. 28-39
Author(s):  
Arno De Caigny ◽  
Kristof Coussement ◽  
Wouter Verbeke ◽  
Khaoula Idbenjra ◽  
Minh Phan

2020 ◽  
Vol 34 (2) ◽  
pp. 273-308 ◽  
Author(s):  
Diego Olaya ◽  
Kristof Coussement ◽  
Wouter Verbeke
Keyword(s):  

2017 ◽  
Vol 17 (11) ◽  
pp. 3416-3421 ◽  
Author(s):  
Ashutosh Mishra ◽  
N. S. Rajput ◽  
Guangjie Han

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