A Bayesian Approach to Weibull Survival Model for Clinical Randomized Censoring Trial Based on MCMC Simulation

2008 ◽  
Author(s):  
Xujie Zhao ◽  
Chao Yu ◽  
Hengqing Tong
Keyword(s):  
Bayesian Approach ◽  
Survival Model ◽  
Mcmc Simulation

scholarly journals A Bayesian Approach to Measurement of Backtest Overfitting

Risks ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 18
Author(s):  
Jiří Witzany
Keyword(s):  
Bayesian Approach ◽  
Performance Indicators ◽  
Multiple Testing ◽  
Historical Data ◽  
Broad Class ◽  
Investment Strategies ◽  
Robust Estimates ◽  
Expected Performance ◽  
Mcmc Simulation ◽  
Naive Approach

Quantitative investment strategies are often selected from a broad class of candidate models estimated and tested on historical data. Standard statistical techniques to prevent model overfitting such as out-sample backtesting turn out to be unreliable in situations when the selection is based on results of too many models tested on the holdout sample. There is an ongoing discussion of how to estimate the probability of backtest overfitting and adjust the expected performance indicators such as the Sharpe ratio in order to reflect properly the effect of multiple testing. We propose a consistent Bayesian approach that yields the desired robust estimates on the basis of a Markov chain Monte Carlo (MCMC) simulation. The approach is tested on a class of technical trading strategies where a seemingly profitable strategy can be selected in the naïve approach.

scholarly journals Bayesian Analysis of Topp-Leone Generalized Exponential Distribution

2018 ◽  
Vol 47 (4) ◽  
pp. 1-15
Author(s):  
Najrullah Khan ◽  
Athar Ali Khan
Keyword(s):  
Bayesian Analysis ◽  
Exponential Distribution ◽  
Bayesian Approach ◽  
Survival Data ◽  
Survival Model ◽  
Generalized Exponential Distribution ◽  
Data Set ◽  
Simulation Tools ◽  
Support Set ◽  
Optimization And Simulation

The Topp-Leone distribution was introduced by Topp-Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized Exponential distribution. Since, Topp-Leone distribution contains only one parameter and its support set is restricted to (0,1), because of this, in most practical situations it is not a better fit for the lifetime modelling. So an extension of this distribution is required. A Bayesian approach has been adopted to fit this model as survival model. A real survival data set is used to illustrate. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools.

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