scholarly journals Conditional transformation models

2013 ◽  
Vol 76 (1) ◽  
pp. 3-27 ◽  
Author(s):  
Torsten Hothorn ◽  
Thomas Kneib ◽  
Peter Bühlmann
Keyword(s):  
Transformation Models ◽  
Conditional Transformation

scholarly journals Deep Conditional Transformation Models

2021 ◽  
pp. 3-18
Author(s):  
Philipp F. M. Baumann ◽  
Torsten Hothorn ◽  
David Rügamer
Keyword(s):  
Transformation Models ◽  
Conditional Transformation

Multivariate Conditional Transformation Models. Application to Thyroid-Related Hormones

2021 ◽  
pp. 341-351
Author(s):  
Carla Díaz-Louzao ◽  
Óscar Lado-Baleato ◽  
Francisco Gude ◽  
Carmen Cadarso-Suárez
Keyword(s):  
Transformation Models ◽  
Conditional Transformation

scholarly journals Conditional Transformation Models for Survivor Function Estimation

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Lisa Möst ◽  
Torsten Hothorn
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Patient Characteristics ◽  
Patient Specific ◽  
Survival Times ◽  
Transformation Models ◽  
Survivor Function ◽  
Conditional Transformation ◽  
The Impact ◽  
Survivor Functions

AbstractIn survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient’s risk than summary statistics, such as median survival time. Nevertheless, standard methods for analysing survival data seldom estimate the survivor function directly. Therefore, we propose the application of conditional transformation models (CTMs) for the estimation of the conditional distribution function of survival times given a set of patient characteristics. We used the inverse probability of censoring weighting approach to account for right-censored observations. Our proposed modelling approach allows the prediction of patient-specific survivor functions. In addition, CTMs constitute a flexible model class that is able to deal with proportional as well as non-proportional hazards. The well-known Cox model is included in the class of CTMs as a special case. We investigated the performance of CTMs in survival data analysis in a simulation that included proportional and non-proportional hazard settings and different scenarios of explanatory variables. Furthermore, we re-analysed the survival times of patients suffering from chronic myelogenous leukaemia and studied the impact of the proportional hazards assumption on previously published results.

scholarly journals Multivariate conditional transformation models

2020 ◽  
Author(s):  
Nadja Klein ◽  
Torsten Hothorn ◽  
Luisa Barbanti ◽  
Thomas Kneib
Keyword(s):  
Transformation Models ◽  
Conditional Transformation

Quantum algorithm for measuring the energy of n qubits with unknown pair-interactions

2002 ◽  
Vol 2 (3) ◽  
pp. 198-207
Author(s):  
D. Janzing
Keyword(s):  
Quantum State ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Pair Interaction ◽  
Phase Estimation ◽  
Solid States ◽  
Energy Gaps ◽  
Large N ◽  
Conditional Transformation ◽  
Essential Assumption

The well-known algorithm for quantum phase estimation requires that the considered unitary is available as a conditional transformation depending on the quantum state of an ancilla register. We present an algorithm converting an unknown n-qubit pair-interaction Hamiltonian into a conditional one such that standard phase estimation can be applied to measure the energy. Our essential assumption is that the considered system can be brought into interaction with a quantum computer. For large n the algorithm could still be applicable for estimating the density of energy states and might therefore be useful for finding energy gaps in solid states.

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
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