Parameter Estimation: Difference between revisions
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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
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.
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
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 .
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
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