Pages that link to "Item:Q4262899"
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The following pages link to Bayesian analysis for inverse gaussian lifetime data with measures of degradation (Q4262899):
Displaying 18 items.
- Degradation modeling applied to residual lifetime prediction using functional data analysis (Q641105) (← links)
- Accelerated degradation models for failure based on geometric Brownian motion and gamma processes (Q995968) (← links)
- Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods (Q1653357) (← links)
- The transformed inverse Gaussian process as an age- and state-dependent degradation model (Q1985162) (← links)
- Linear Bayes estimators applied to the inverse Gaussian lifetime model (Q2014429) (← links)
- Objective Bayesian analysis accelerated degradation test based on Wiener process models (Q2289279) (← links)
- Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary (Q2381748) (← links)
- Semiparametric inference on a class of Wiener processes (Q3077647) (← links)
- Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need (Q3195235) (← links)
- Bayesian random-effects threshold regression with application to survival data with nonproportional hazards (Q3303589) (← links)
- Stochastic Accelerated Degradation Models Based on a Generalized Cumulative Damage Approach (Q4559439) (← links)
- Objective Bayesian Analysis for Linear Degradation Models (Q4649618) (← links)
- Stochastic methodology for prognostics under continuously varying environmental profiles (Q4969901) (← links)
- Remaining useful life prediction: A multiple product partition approach (Q5042126) (← links)
- Optimal design of accelerated degradation tests based on Wiener process models (Q5124762) (← links)
- A Bivariate Gamma Model for a Latent Degradation Process (Q5419693) (← links)
- A Bayes study of inverse Gaussian based strength models with accelerating stress (Q6074129) (← links)
- Nonparametric Modeling and Prognosis of Condition Monitoring Signals Using Multivariate Gaussian Convolution Processes (Q6622455) (← links)
