Expert Systems with Applications: An International Journal
Comparison procedure of predicting the time to default in behavioural scoring
Expert Systems with Applications: An International Journal
A hybrid GA-ant colony approach for exploring the relationship between IT and firm performance
International Journal of Business Information Systems
Expert Systems with Applications: An International Journal
International Journal of Knowledge Engineering and Soft Data Paradigms
The Duration of Patent Examination at the European Patent Office
Management Science
A Bayesian MCMC approach to survival analysis with doubly-censored data
Computational Statistics & Data Analysis
Applied Survival Analysis: Regression Modeling of Time to Event Data
Applied Survival Analysis: Regression Modeling of Time to Event Data
Application of relevance vector machine and survival probability to machine degradation assessment
Expert Systems with Applications: An International Journal
Analyzing survival curves at a fixed point in time for paired and clustered right-censored data
Computational Statistics & Data Analysis
Machine health prognostics using survival probability and support vector machine
Expert Systems with Applications: An International Journal
A Simulation Optimization Approach to Long-Term Care Capacity Planning
Operations Research
An EM algorithm for the proportional hazards model with doubly censored data
Computational Statistics & Data Analysis
Hi-index | 0.00 |
The main topic in medical statistics is survival time. It is difficult to obtain complete data in studies of survival time because of several reasons. One aspect of such difficulty is related to death of all patients. A study is often completed before the death of all patients. Moreover, incomplete information is kept with respect to death of all patients. Usually, exponential and Weibull distribution are used to estimate actual time of the censored data. In general, censored data models describe situations where there are variables of interest that cannot always be observed directly and may deviate from certain values. This study presents an adaptive neural network ANN approach for improved estimation of right censored data. To show the superiority and advantages of the ANN approach, it has been compared with fuzzy mathematical programming and conventional approaches. It is shown that ANN provides better estimation for right censored data in comparison to fuzzy mathematical programming and exponential and Weibull distributions. This is the first study that utilises ANN for improved estimation of right censored data.