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{{template:LDABOOK|4|Parameter Estimation}}
==Parameter Estimation==


Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis.  More specidically we start with the relatively simple method of [[Probability Plotting]] and continues with the more sophisticated methods of [[Rank Regression]] ( or [[Least Squares]]) and [[Maximum Likelihood]].
Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis.  More specidically we start with the relatively simple method of [[Probability Plotting]] and continues with the more sophisticated methods of [[Rank Regression]] ( or [[Least Squares]]) and [[Maximum Likelihood]].

Revision as of 21:26, 8 February 2012

New format available! This reference is now available in a new format that offers faster page load, improved display for calculations and images, more targeted search and the latest content available as a PDF. As of September 2023, this Reliawiki page will not continue to be updated. Please update all links and bookmarks to the latest reference at help.reliasoft.com/reference/life_data_analysis

Chapter 4: Parameter Estimation


Weibullbox.png

Chapter 4  
Parameter Estimation  

Synthesis-icon.png

Available Software:
Weibull++

Examples icon.png

More Resources:
Weibull++ Examples Collection


Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis. More specidically we start with the relatively simple method of Probability Plotting and continues with the more sophisticated methods of Rank Regression ( or Least Squares) and Maximum Likelihood.


New format available! This reference is now available in a new format that offers faster page load, improved display for calculations and images, more targeted search and the latest content available as a PDF. As of September 2023, this Reliawiki page will not continue to be updated. Please update all links and bookmarks to the latest reference at help.reliasoft.com/reference/life_data_analysis

Chapter 4: Parameter Estimation


Weibullbox.png

Chapter 4  
Parameter Estimation  

Synthesis-icon.png

Available Software:
Weibull++

Examples icon.png

More Resources:
Weibull++ Examples Collection


Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis. More specidically we start with the relatively simple method of Probability Plotting and continues with the more sophisticated methods of Rank Regression ( or Least Squares) and Maximum Likelihood.


Template loop detected: Template:Probability Plotting


Template loop detected: Template:Rank Regression or Least Squares Parameter Estimation

ReliaSoft's Alternate Ranking Method (RRM)

When analyzing interval data, it is commonplace to assume that the actual failure time occurred at the midpoint of the interval. To be more conservative, you can use the starting point of the interval or you can use the end point of the interval to be most optimistic. Weibull++ allows you to employ ReliaSoft's ranking method (RRM) when analyzing interval data. Using an iterative process, this ranking method is an improvement over the standard ranking method (SRM). For more details on this method see ReliaSoft's Alternate Ranking Method .


Template loop detected: Template:MLE Parameter Estimation


Template loop detected: Template:Bayesian Parameter Estimation Methods


New format available! This reference is now available in a new format that offers faster page load, improved display for calculations and images, more targeted search and the latest content available as a PDF. As of September 2023, this Reliawiki page will not continue to be updated. Please update all links and bookmarks to the latest reference at help.reliasoft.com/reference/life_data_analysis

Chapter 4: Parameter Estimation


Weibullbox.png

Chapter 4  
Parameter Estimation  

Synthesis-icon.png

Available Software:
Weibull++

Examples icon.png

More Resources:
Weibull++ Examples Collection


Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis. More specidically we start with the relatively simple method of Probability Plotting and continues with the more sophisticated methods of Rank Regression ( or Least Squares) and Maximum Likelihood.


Template loop detected: Template:Probability Plotting


Template loop detected: Template:Rank Regression or Least Squares Parameter Estimation

ReliaSoft's Alternate Ranking Method (RRM)

When analyzing interval data, it is commonplace to assume that the actual failure time occurred at the midpoint of the interval. To be more conservative, you can use the starting point of the interval or you can use the end point of the interval to be most optimistic. Weibull++ allows you to employ ReliaSoft's ranking method (RRM) when analyzing interval data. Using an iterative process, this ranking method is an improvement over the standard ranking method (SRM). For more details on this method see ReliaSoft's Alternate Ranking Method .


Template loop detected: Template:MLE Parameter Estimation


Template loop detected: Template:Bayesian Parameter Estimation Methods

ReliaSoft's Alternate Ranking Method (RRM)

When analyzing interval data, it is commonplace to assume that the actual failure time occurred at the midpoint of the interval. To be more conservative, you can use the starting point of the interval or you can use the end point of the interval to be most optimistic. Weibull++ allows you to employ ReliaSoft's ranking method (RRM) when analyzing interval data. Using an iterative process, this ranking method is an improvement over the standard ranking method (SRM). For more details on this method see ReliaSoft's Alternate Ranking Method .


New format available! This reference is now available in a new format that offers faster page load, improved display for calculations and images, more targeted search and the latest content available as a PDF. As of September 2023, this Reliawiki page will not continue to be updated. Please update all links and bookmarks to the latest reference at help.reliasoft.com/reference/life_data_analysis

Chapter 4: Parameter Estimation


Weibullbox.png

Chapter 4  
Parameter Estimation  

Synthesis-icon.png

Available Software:
Weibull++

Examples icon.png

More Resources:
Weibull++ Examples Collection


Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis. More specidically we start with the relatively simple method of Probability Plotting and continues with the more sophisticated methods of Rank Regression ( or Least Squares) and Maximum Likelihood.


Template loop detected: Template:Probability Plotting


Template loop detected: Template:Rank Regression or Least Squares Parameter Estimation

ReliaSoft's Alternate Ranking Method (RRM)

When analyzing interval data, it is commonplace to assume that the actual failure time occurred at the midpoint of the interval. To be more conservative, you can use the starting point of the interval or you can use the end point of the interval to be most optimistic. Weibull++ allows you to employ ReliaSoft's ranking method (RRM) when analyzing interval data. Using an iterative process, this ranking method is an improvement over the standard ranking method (SRM). For more details on this method see ReliaSoft's Alternate Ranking Method .


Template loop detected: Template:MLE Parameter Estimation


Template loop detected: Template:Bayesian Parameter Estimation Methods


New format available! This reference is now available in a new format that offers faster page load, improved display for calculations and images, more targeted search and the latest content available as a PDF. As of September 2023, this Reliawiki page will not continue to be updated. Please update all links and bookmarks to the latest reference at help.reliasoft.com/reference/life_data_analysis

Chapter 4: Parameter Estimation


Weibullbox.png

Chapter 4  
Parameter Estimation  

Synthesis-icon.png

Available Software:
Weibull++

Examples icon.png

More Resources:
Weibull++ Examples Collection


Parameter estimation refers to the process of using sample data (in our case times-to-failure or suceess data) to estimate the parameters of the selected distribution. Several parameter estimation methods are available. This section presents an overview of the available methods used in life data analysis. More specidically we start with the relatively simple method of Probability Plotting and continues with the more sophisticated methods of Rank Regression ( or Least Squares) and Maximum Likelihood.


Template loop detected: Template:Probability Plotting


Template loop detected: Template:Rank Regression or Least Squares Parameter Estimation

ReliaSoft's Alternate Ranking Method (RRM)

When analyzing interval data, it is commonplace to assume that the actual failure time occurred at the midpoint of the interval. To be more conservative, you can use the starting point of the interval or you can use the end point of the interval to be most optimistic. Weibull++ allows you to employ ReliaSoft's ranking method (RRM) when analyzing interval data. Using an iterative process, this ranking method is an improvement over the standard ranking method (SRM). For more details on this method see ReliaSoft's Alternate Ranking Method .


Template loop detected: Template:MLE Parameter Estimation


Template loop detected: Template:Bayesian Parameter Estimation Methods