Sarhan, 2019 - Google Patents
Bayesian analysis of the discrete two-parameter bathtub hazard distributionSarhan, 2019
View PDF- Document ID
- 4072354427127779398
- Author
- Sarhan A
- Publication year
- Publication venue
- Journal of Mathematical Sciences and Modelling
External Links
Snippet
A new discrete two-parameter bathtub hazard distribution is proposed by Sarhan\cite {Sarhan-2017}. This paper uses Bayes method to estimate the two unknown parameters and the reliability measures of this distribution. The joint posterior distribution of the model …
- 238000009826 distribution 0 title abstract description 65
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lüdtke et al. | Multiple imputation of missing data in multilevel designs: A comparison of different strategies. | |
Yuan et al. | Information matrices and standard errors for MLEs of item parameters in IRT | |
Zhao et al. | Validation metric based on Mahalanobis distance for models with multiple correlated responses | |
CN107679566A (en) | A kind of Bayesian network parameters learning method for merging expert's priori | |
Koller et al. | Nonparametric tests for the Rasch model: explanation, development, and application of quasi-exact tests for small samples | |
Ndanguza et al. | Analysis of bias in an Ebola epidemic model by extended Kalman filter approach | |
Kohns et al. | Horseshoe prior Bayesian quantile regression | |
Xie et al. | Statistical uncertainty analysis for stochastic simulation with dependent input models | |
Asgharzadeh et al. | Point and interval estimation for the logistic distribution based on record data | |
Kharazmi et al. | Harmonic mixture-G family of distributions: survival regression, simulation by likelihood, bootstrap and bayesian discussion with MCMC algorithm | |
Chaturvedi et al. | Statistical inferences of type-II progressively hybrid censored fuzzy data with Rayleigh distribution | |
Sarhan | Bayesian analysis of the discrete two-parameter bathtub hazard distribution | |
Meng et al. | A mixed stochastic approximation EM (MSAEM) algorithm for the estimation of the four-parameter normal ogive model | |
Gray | Use of the Bayesian family of methods to correct for effects of exposure measurement error in polynomial regression models | |
Rabe et al. | ROC asymmetry is not diagnostic of unequal residual variance in gaussian signal detection theory | |
Redivo et al. | Bayesian estimation of a quantile-based factor model | |
Walker et al. | Bayesian parametric inference in a nonparametric framework | |
Waelbroeck | Computational issues in the sequential probit model: a Monte Carlo study | |
Talmor et al. | System reliability estimation with failure modes analysis applying bayesian approach-case study | |
Marhadi et al. | Quantifying uncertainty in statistical distribution of small sample data using bayesian inference of unbounded johnson distribution | |
Rougier et al. | Formal Bayes methods for model calibration with uncertainty | |
Kellen et al. | On the measurement of criterion noise in signal detection theory: Reply to Benjamin (2013). | |
Aljohani | Statistical Inference of Power Hazard Rate Distribution in the Presence of Competing Risks Model with Application | |
Roy | Sequential-adaptive design of computer experiments for the estimation of percentiles | |
Cantaluppi et al. | A prediction method for ordinal consistent partial least squares |