Fuzzy availability assessment for a discrete time repairable multi-state series-parallel system

2016 ◽  
Vol 30 (5) ◽  
pp. 2663-2675 ◽  
Author(s):  
Linmin Hu ◽  
Peng Su
Keyword(s):  
Discrete Time ◽  
Parallel System ◽  
Fuzzy Availability ◽  
Availability Assessment

scholarly journals Fuzzy Availability Assessment for a Repairable Multistate Series-Parallel System

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Linmin Hu ◽  
Dequan Yue ◽  
Ruiling Tian
Keyword(s):  
Parametric Programming ◽  
Parallel System ◽  
Function Technique ◽  
Extension Principle ◽  
Programming Technique ◽  
Assessment Approach ◽  
Fuzzy Availability ◽  
Special Assessment ◽  
Fuzzy State ◽  
Availability Assessment

This paper considers a repairable multistate series-parallel system (RMSSPS) with fuzzy parameters. It is assumed that the system components are independent, and their state transition rates and performance rates are fuzzy values. The fuzzy universal generating function technique is adopted to determine fuzzy state probability and fuzzy performance rate of the system. On the basis ofα-cut approach and the extension principle, parametric programming technique is employed to obtain theα-cuts of some indices for the system. The system fuzzy availability is defined as the ability of the system to satisfy fuzzy consumer demand. A special assessment approach is developed for evaluating the fuzzy steady-state availability of the system with the fuzzy demand. A flow transmission system with three components is presented to demonstrate the validity of the proposed method.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Sign in / Sign up

Export Citation Format

Share Document