# Hazard Models

May 29 • Notes • 1396 Views • 3 Comments on Hazard Models

Hazard Models:

There are many hazard models. Some of them are explained below.

1.    Proportional Hzard Models(PHM):

• It is a multivariate regression analysis.
• This is proposed by Cox in 1972.
•  The model has been developed for different applications in life tine analysis. This model estimates the effects of different covariates  like survival or reliability covariates influencing TTF of a system.
•  Mainly , all the models are based on proportional hazard models.
•  PHM is a very useful technique  as it helps in investigating explanatory variables in hazard of assets.
• It is available for both static and dynamic explanatory variables.
• This models handles truncated data, non truncated data and tied values.

2.    Stratified Proportional Hzard Models(SPHM):

•  Based on the discrete values of single covariate or combination  of discrete values of a set of covariates, the population is divided into q levels in case of SPHM.
•  It is the useful extensions of PHM for its applications  in different situations.
•  This is distribution free.

•  when a system after repair is better than it was just before the repair, but not as  good as new this is additive model.
•  The hazard that is not zero at time zero is said by this model
•  It canot handle tied values.

•  The hazard of an individual is considered as mixed model ,which contains both additive and multiplicative component.
•  Sometimes it gives much better fit to the data as compared to PHM.

5.    Accelerated Failure Time Model:

• The Accelerated Failure Time Model (AFTM) is the most common approaches which is used in obtaining reliability and failure rate estimates of devices and components in a much shorter time.

6.    Extended Hzard Regression Model:

•  This is developed in 1985.
• This includes both PHM and AFTM.

Q1. Explain some of the five hazard models.

Ans.         Hazard Models:

There are many hazard models. Some of them are explained below.

1.    Proportional Hzard Models(PHM):

•  It is a multivariate regression analysis.
• This is proposed by Cox in 1972.
• The model has been developed for different applications in life tine analysis. This model estimates the effects of different covariates  like survival or reliability covariates influencing TTF of a system.
•  Mainly , all the models are based on proportional hazard models.
• PHM is a very useful technique  as it helps in investigating explanatory variables in hazard of assets.
•  It is available for both static and dynamic explanatory variables.
•  This models handles truncated data, non truncated data and tied values.

2.    Stratified Proportional Hzard Models(SPHM):

• Based on the discrete values of single covariate or combination  of discrete values of a set of covariates, the population is divided into q levels in case of SPHM.
• It is the useful extensions of PHM for its applications  in different situations.
• This is distribution free.

• when a system after repair is better than it was just before the repair, but not as  good as new this is additive model.
•  The hazard that is not zero at time zero is said by this model
•  It canot handle tied values.

• ØThe hazard of an individual is considered as mixed model ,which contains both additive and multiplicative component.
•  Sometimes it gives much better fit to the data as compared to PHM.

5.    Accelerated Failure Time Model:

•  The Accelerated Failure Time Model (AFTM) is the most common approaches which is used in obtaining reliability and failure rate estimates of devices and components in a much shorter time.

### 3 Responses to Hazard Models

1. Mitali Panda says:

This article is much different from other as it is giving a brief detail of hazard models, which are use to check out the life line of devices and its componenets after regressive uses of them.

2. patlakshi Jha says:

This article gives information about the hazard model. This is important to know what are hazards model. This post explains clearly and we can get lot of information about the same.

3. Shilpa Ranjan says: