Template:Non-parametric LDA introduction - Revision history

Richard House: Redirected page to Non-Parametric Life Data Analysis

2012-08-20T23:16:02Z

Redirected page to Non-Parametric Life Data Analysis

← Older revision		Revision as of 23:16, 20 August 2012
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	~~== Introduction ==~~		#REDIRECT [[Non-Parametric_Life_Data_Analysis]]

	Non-parametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with non-parametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a non-parametric analysis before moving on to the assumption of an underlying distribution.

	There are several methods for conducting a non-parametric analysis. In Weibull++, this includes the Kaplan-Meier, actuarial-simple and actuarial-standard methods. A method for attaching confidence bounds to the results of these non-parametric analysis techniques can also be developed. The basis of non-parametric life data analysis is the empirical <span class="texhtml">''c''''d''''f''</span>  function, which is given by:

	~~::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>~~

	~~Note that this is similar to the Bernard's approximation of the median ranks, as discussed in [[Parameter Estimation~~]]~~ chapter. The following non-parametric analysis methods are essentially variations of this concept.~~

Kate Racaza at 22:14, 9 March 2012

2012-03-09T22:14:01Z

← Older revision		Revision as of 22:14, 9 March 2012
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	Non-parametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with non-parametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a non-parametric analysis before moving on to the assumption of an underlying distribution.		Non-parametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with non-parametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a non-parametric analysis before moving on to the assumption of an underlying distribution.

	There are several methods for conducting a non-parametric analysis. In Weibull++, this includes the Kaplan-Meier, simple actuarial ~~and~~ standard ~~actuarial~~ methods. A method for attaching confidence bounds to the results of these non-parametric analysis techniques can also be developed. The basis of non-parametric life data analysis is the empirical <span class="texhtml">''c''''d''''f''</span>  function, which is given by:		There are several methods for conducting a non-parametric analysis. In Weibull++, this includes the Kaplan-Meier, actuarial-simple and actuarial-standard methods. A method for attaching confidence bounds to the results of these non-parametric analysis techniques can also be developed. The basis of non-parametric life data analysis is the empirical <span class="texhtml">''c''''d''''f''</span>  function, which is given by:

	::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>		::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>

	Note that this is similar to the Bernard's approximation of the median ranks, as discussed in [[Parameter Estimation]] chapter. The following non-parametric analysis methods are essentially variations of this concept.		Note that this is similar to the Bernard's approximation of the median ranks, as discussed in [[Parameter Estimation]] chapter. The following non-parametric analysis methods are essentially variations of this concept.

Kate Racaza at 22:06, 9 March 2012

2012-03-09T22:06:36Z

← Older revision		Revision as of 22:06, 9 March 2012
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	==Introduction==		== Introduction ==

	~~Nonparametric~~ analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with ~~nonparametric~~ analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a ~~nonparametric~~ analysis before moving on to the assumption of an underlying distribution.		Non-parametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with non-parametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a non-parametric analysis before moving on to the assumption of an underlying distribution.

	There are several methods for conducting a ~~nonparametric~~ analysis, ~~including~~ the Kaplan-Meier, simple actuarial, and standard actuarial methods. A method for attaching confidence bounds to the results of these ~~nonparametric~~ analysis techniques can also be developed. The basis of ~~nonparametric~~ life data analysis is the empirical <~~math~~>~~cdf~~</~~math~~> function, which is given by:		There are several methods for conducting a non-parametric analysis. In Weibull++, this includes the Kaplan-Meier, simple actuarial and standard actuarial methods. A method for attaching confidence bounds to the results of these non-parametric analysis techniques can also be developed. The basis of non-parametric life data analysis is the empirical <span class="texhtml">''c''''d''''f''</span>  function, which is given by:

	::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>		::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>

	Note that this is similar to the Bernard's approximation of the median ranks, as discussed in ~~Chapter~~ [[Parameter Estimation]]. The following ~~nonparametric~~ analysis methods are essentially variations of this concept.		Note that this is similar to the Bernard's approximation of the median ranks, as discussed in [[Parameter Estimation]] chapter. The following non-parametric analysis methods are essentially variations of this concept.

Harry Guo: /* Introduction */

2012-02-20T20:38:39Z

Introduction

← Older revision		Revision as of 20:38, 20 February 2012
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	Nonparametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with nonparametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a nonparametric analysis before moving on to the assumption of an underlying distribution.		Nonparametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with nonparametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a nonparametric analysis before moving on to the assumption of an underlying distribution.

	There are several methods for conducting a nonparametric analysis, including the Kaplan-Meier, simple actuarial, and standard actuarial methods. A method for attaching confidence bounds to the results of these nonparametric analysis techniques can also be developed. The basis of nonparametric life data analysis is the empirical <math>cdf</math> function, which is given by:		There are several methods for conducting a nonparametric analysis, including the Kaplan-Meier, simple actuarial, and standard actuarial methods. A method for attaching confidence bounds to the results of these nonparametric analysis techniques can also be developed. The basis of nonparametric life data analysis is the empirical <math>cdf</math> function, which is given by:


	::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>		::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>

			Note that this is similar to the Bernard's approximation of the median ranks, as discussed in Chapter [[Parameter Estimation]]. The following nonparametric analysis methods are essentially variations of this concept.
	Note that this is similar to the Bernard's approximation of the median ranks, as discussed in Chapter 3. The following nonparametric analysis methods are essentially variations of this concept.

Harry Guo: Created page with '==Introduction== Nonparametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. T…'

2012-02-20T18:47:29Z

Created page with '==Introduction== Nonparametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. T…'

New page

==Introduction==

Nonparametric analysis allows the user to analyze data without assuming an underlying distribution. This can have certain advantages as well as disadvantages. The ability to analyze data without assuming an underlying life distribution avoids the potentially large errors brought about by making incorrect assumptions about the distribution. On the other hand, the confidence bounds associated with nonparametric analysis are usually much wider than those calculated via parametric analysis, and predictions outside the range of the observations are not possible. Some practitioners recommend that any set of life data should first be subjected to a nonparametric analysis before moving on to the assumption of an underlying distribution.
There are several methods for conducting a nonparametric analysis, including the Kaplan-Meier, simple actuarial, and standard actuarial methods. A method for attaching confidence bounds to the results of these nonparametric analysis techniques can also be developed. The basis of nonparametric life data analysis is the empirical <math>cdf</math> function, which is given by:

::<math>\widehat{F}(t)=\frac{observations\le t}{n}</math>

Note that this is similar to the Bernard's approximation of the median ranks, as discussed in Chapter 3. The following nonparametric analysis methods are essentially variations of this concept.