Template:Weibull Recurrent Events Data Introduction - Revision history

Kate Racaza: Redirected page to Recurrent Event Data Analysis

2012-06-29T07:48:03Z

Redirected page to Recurrent Event Data Analysis

← Older revision		Revision as of 07:48, 29 June 2012
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	~~== Introduction ==~~		#REDIRECT [[Recurrent Event Data Analysis]]

	Recurrent Event Data Analysis ~~(RDA) is used in various applied fields such as reliability, medicine, social sciences, economics, business and criminology.~~

	Whereas in life data analysis (LDA) it was assumed that events (failures) were independent and identically distributed (iid), there are many cases where events are dependent and not identically distributed (such as repairable system data) or where the analyst is interested in modeling the number of occurrences of events over time rather than the length of time prior to the first event, as in LDA.

	~~Weibull++ provides both parametric and non-parametric approaches to analyze such data.~~

	:*The non-parametric approach is based on the well-known Mean Cumulative Function (MCF). The Weibull++ module for this type of analysis builds upon the work of Dr. Wayne Nelson, who has written extensively on the calculation and applications of MCF [[Appendix: Weibull References\|[31]]].

	:*The parametric approach is based on the General Renewal Process (GRP) model, which is particularly useful in understanding the effects of the repairs on the age of a system. Traditionally, the commonly used models for analyzing repairable systems data are the perfect renewal processes (PRP), which corresponds to perfect repairs, and the nonhomogeneous Poisson processes (NHPP), which corresponds to minimal repairs. However, most repair activities may realistically not result in such extreme situations but in a complicated intermediate one (general repair or imperfect repair/maintenance), which are well treated with the GRP model.

Kate Racaza at 17:39, 8 March 2012

2012-03-08T17:39:31Z

← Older revision		Revision as of 17:39, 8 March 2012
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	==Introduction==		== Introduction ==

	Recurrent Event Data Analysis (RDA)~~, also called Recurrence Data Analysis, can be~~ used in various applied fields such as reliability, medicine, social sciences, economics, business and criminology.		Recurrent Event Data Analysis (RDA) is used in various applied fields such as reliability, medicine, social sciences, economics, business and criminology.

	Whereas in life data analysis (LDA) it was assumed that events (failures) were independent and identically distributed (iid), there are many cases where events are dependent and not identically distributed (such as repairable system data) or where the analyst is interested in modeling the number of occurrences of events over time rather than the length of time prior to the first event, as in LDA.		Whereas in life data analysis (LDA) it was assumed that events (failures) were independent and identically distributed (iid), there are many cases where events are dependent and not identically distributed (such as repairable system data) or where the analyst is interested in modeling the number of occurrences of events over time rather than the length of time prior to the first event, as in LDA.

	Weibull++ provides both parametric and non-parametric approaches to analyze such data.		Weibull++ provides both parametric and non-parametric approaches to analyze such data.

	:*The non-parametric approach is based on the well-known Mean Cumulative Function (MCF). The Weibull++ module for this type of analysis builds upon the work of Dr. Wayne Nelson, who has written extensively on the calculation and applications of MCF [[Appendix: Weibull References\|[31]]].		:*The non-parametric approach is based on the well-known Mean Cumulative Function (MCF). The Weibull++ module for this type of analysis builds upon the work of Dr. Wayne Nelson, who has written extensively on the calculation and applications of MCF [[Appendix: Weibull References\|[31]]].

	:*The parametric approach is based on the General Renewal Process (GRP) model, which is particularly useful in understanding the effects of the repairs on the age of a system. Traditionally, the commonly used models for analyzing repairable systems data are perfect renewal processes (PRP), ~~corresponding~~ to perfect repairs, and nonhomogeneous Poisson processes (NHPP), ~~corresponding~~ to minimal repairs. However, most repair activities may realistically not result in such extreme situations but in a complicated intermediate one (general repair or imperfect repair/maintenance), which are well treated with the GRP model.		:*The parametric approach is based on the General Renewal Process (GRP) model, which is particularly useful in understanding the effects of the repairs on the age of a system. Traditionally, the commonly used models for analyzing repairable systems data are the perfect renewal processes (PRP), which corresponds to perfect repairs, and the nonhomogeneous Poisson processes (NHPP), which corresponds to minimal repairs. However, most repair activities may realistically not result in such extreme situations but in a complicated intermediate one (general repair or imperfect repair/maintenance), which are well treated with the GRP model.

Harry Guo: Created page with '==Introduction== Recurrent Event Data Analysis (RDA), also called Recurrence Data Analysis, can be used in various applied fields such as reliability, medicine, social sciences,…'

2012-02-22T17:07:27Z

Created page with '==Introduction== Recurrent Event Data Analysis (RDA), also called Recurrence Data Analysis, can be used in various applied fields such as reliability, medicine, social sciences,…'

New page

==Introduction==

Recurrent Event Data Analysis (RDA), also called Recurrence Data Analysis, can be used in various applied fields such as reliability, medicine, social sciences, economics, business and criminology.

Whereas in life data analysis (LDA) it was assumed that events (failures) were independent and identically distributed (iid), there are many cases where events are dependent and not identically distributed (such as repairable system data) or where the analyst is interested in modeling the number of occurrences of events over time rather than the length of time prior to the first event, as in LDA.

Weibull++ provides both parametric and non-parametric approaches to analyze such data.

:*The non-parametric approach is based on the well-known Mean Cumulative Function (MCF). The Weibull++ module for this type of analysis builds upon the work of Dr. Wayne Nelson, who has written extensively on the calculation and applications of MCF [[Appendix: Weibull References|[31]]].

:*The parametric approach is based on the General Renewal Process (GRP) model, which is particularly useful in understanding the effects of the repairs on the age of a system. Traditionally, the commonly used models for analyzing repairable systems data are perfect renewal processes (PRP), corresponding to perfect repairs, and nonhomogeneous Poisson processes (NHPP), corresponding to minimal repairs. However, most repair activities may realistically not result in such extreme situations but in a complicated intermediate one (general repair or imperfect repair/maintenance), which are well treated with the GRP model.