1 edition of **Bayesian inference for stress-strength models with explanatory variables** found in the catalog.

Bayesian inference for stress-strength models with explanatory variables

- 346 Want to read
- 14 Currently reading

Published
**1989**
by University of Toronto, Dept. of Statistics in Toronto
.

Written in English

- Analysis of variance.,
- Bayesian statistical decision theory.,
- Multivariate analysis.

**Edition Notes**

Includes bibliographical references.

Statement | by G.K. Bhattacharyya ... [et al.]. |

Series | Technical report series / University of Toronto Department of Statistics -- no. 13, Technical report (University of Toronto. Dept. of Statistics) -- no. 13 (1989) |

Contributions | Bhattacharyya, G. |

Classifications | |
---|---|

LC Classifications | QA279.5 .B37 1989 |

The Physical Object | |

Pagination | 18 p. : |

Number of Pages | 18 |

ID Numbers | |

Open Library | OL20747489M |

– The hazard function, used for regression in survival analysis, can lend more insight into the failure mechanism than linear regression. BIOST , Lecture 15 4. Censoring Censoring is present when we have some information about a subject’s event time, but we don’t know the exact event Size: KB. [Wiley Series in Probability and Statistics] Michael R. Chernick - Bootstrap methods- a guide for practitioners and researchers ( Wiley-Interscience).pdf.

Statistical inference guides the selection of appropriate statistical models. Models and data interact in statistical work. Inference from data can be thought of as the process of selecting a reasonable model, including a statement in probability language of how confident one can be about the selection. Modeling Survival Data Using Frailty Models Here the variable of interest (response variable, T) is time to death or relapse, I is an indicator (1-dead or relapsed, 0-alive or disease free). PAge, PSex, hospital, g are the covariates indicating patient age, patient sex (1male, 0-female), disease group (1-ALL, 2-AML-low risk, 3-AML-high risk).

An oxidation self-heating process of sulfurized rust usually results in a fire or an explosion in crude oil tanks due to the oil’s maximum temperature (T max) exceeding the critical temperature at which the fire and explosion previous studies have shown that T max is determined by the five main factors including water content, mass of sulfurized rust, operating temperature, air. This article surveys the application of gamma processes in maintenance. Since the introduction of the gamma process in the area of reliability in , it has been increasingly used to model stochastic deterioration for optimising maintenance. Because gamma processes are well suited for modelling the temporal variability of deterioration, they have proven to be useful in determining optimal.

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Abstract. In this paper, we consider the Bayesian inference on the stress-strength parameter \(R = P(Y variables X and Y have different scale parameters and (a) a common shape parameter or (b) different shape parameters. Moreover, both stress and strength may depend Author: Debasis Kundu.

Conclusion. In this study, we have systematically explored statistical inference procedures for record values from the Weibull model.

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The purpose of this page is to provide resources in the rapidly growing area of computational statistics and probability for decision making under uncertainties. Here you can find a collection of teaching and research resources on various topics related to computational statistics and probability useful in probabilistic modeling processes.

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