An application of robust Bayesian analysis to a medical experiment

1994 ◽  
Vol 40 (2-3) ◽  
pp. 221-232 ◽  
Author(s):  
J.B. Kadane
Test ◽  
1994 ◽  
Vol 3 (2) ◽  
pp. 73-86 ◽  
Author(s):  
Cinzia Carota ◽  
Fabrizio Ruggeri

2010 ◽  
Vol 53 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Mohammad Jafari Jozani ◽  
Éric Marchand ◽  
Ahmad Parsian

2005 ◽  
Vol 11 (2) ◽  
pp. 361-374 ◽  
Author(s):  
E. Gómez-Déniz ◽  
L. Bermúdez ◽  
I. Morillo

ABSTRACTThe use of classical bonus–malus systems entails very high maluses and other problems which, during recent years, have been criticised by actuaries. To avoid these problems, new bonus–malus models have been developed. For instance, it is well known that the use of an exponential loss function reduces the differences between overcharges and undercharges, solving the problem of high maluses. In order to measure the sensitivity of the exponential bonus–malus system, and according to robust Bayesian analysis, we first model the structure function by specifying a subclass of the generalised moments class. We then examine the range of relativities for each prior. Finally, we illustrate our method with a numerical example based on real data.


METRON ◽  
2013 ◽  
Vol 72 (1) ◽  
pp. 77-95 ◽  
Author(s):  
Anoop Chaturvedi ◽  
Manaswini Pati ◽  
Sanjeev K. Tomer

2018 ◽  
Vol 48 (1) ◽  
pp. 38-55
Author(s):  
M. S. Panwar ◽  
Sanjeev K Tomer

In this paper, we consider robust Bayesian analysis of lifetime data from the Maxwell distribution assuming an $\varepsilon$-contamination class of prior distributions for the parameter. We obtain robust Bayes estimates of the parameter and mean lifetime under squared error and LINEX loss functions in presence of uncensored as well as Type-I progressively hybrid censored lifetime data. A real data set is analysed for numerical illustrations.


Sign in / Sign up

Export Citation Format

Share Document