Response transformation models

2017 ◽  
pp. 174-200
Author(s):  
T.J. Hastie ◽  
R.J. Tibshirani
Keyword(s):  
Transformation Models ◽  
Response Transformation

scholarly journals Discrete choice models with response transformation : An application to beverage choice

2015 ◽  
Vol 3 (2) ◽  
pp. 135
Author(s):  
Sunil K Sapra
Keyword(s):  
Discrete Choice ◽  
Categorical Data ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Continuous Data ◽  
Binary Response ◽  
Transformation Models ◽  
Response Data ◽  
Box Cox Transformation ◽  
Response Transformation

<p>The paper studies various response transformation models for discrete choice and categorical data. These response transformation models are fitted to binary response data on beverage choice. Several models are compared, and the best model is selected using AICs and deviances. The transformations include extensions of the widely used Box-Cox transformation to Normality for continuous data to categorical data. The econometric techniques employed in the paper are widely applicable to the analysis of count, binary response, and duration types of data encountered in business and economics.</p>

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

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.

Local transformation models for speech recognition

2006 ◽  
Author(s):  
Antonio Miguel ◽  
Eduardo Lleida ◽  
Alfons Juan ◽  
Luis Buera ◽  
Alfonso Ortega ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Local Transformation ◽  
Transformation Models

Probabilistic transformation models for preconsolidation stress based on clay index properties

2017 ◽  
Vol 226 ◽  
pp. 33-43 ◽  
Author(s):  
Jianye Ching ◽  
Tsai-Jung Wu
Keyword(s):  
Index Properties ◽  
Transformation Models

The use of thermal analysis techniques for the validation of phase transformation models

1994 ◽  
Vol 65 (3) ◽  
pp. 103-109 ◽  
Author(s):  
Andre Bodin ◽  
Piet D. Marchal
Keyword(s):  
Thermal Analysis ◽  
Phase Transformation ◽  
Transformation Models ◽  
Analysis Techniques ◽  
Thermal Analysis Techniques

scholarly journals On the Structure of Transformation Models

1989 ◽  
Vol 17 (1) ◽  
pp. 195-208 ◽  
Author(s):  
Niels Christian ◽  
Bang Jespersen
Keyword(s):  
Transformation Models
Sign in / Sign up

Export Citation Format

Share Document