Parameter Estimation: Difference between revisions
Kate Racaza (talk | contribs) No edit summary |
Kate Racaza (talk | contribs) No edit summary |
||
| Line 1: | Line 1: | ||
{{template:LDABOOK|4|Parameter Estimation}} | {{template:LDABOOK|4|Parameter Estimation}} | ||
Parameter estimation refers to the process of using sample data (in our case times-to-failure or success 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 specifically, we start with the relatively simple method of [[Probability Plotting]] and continue 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 success 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 specifically, we start with the relatively simple method of [[Probability Plotting]] and continue with the more sophisticated methods of [[Rank Regression]] (or [[Least Squares]]) and [[Maximum Likelihood]]. | ||
Revision as of 20:49, 6 March 2012
Parameter estimation refers to the process of using sample data (in our case times-to-failure or success 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 specifically, we start with the relatively simple method of Probability Plotting and continue 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 success 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 specifically, we start with the relatively simple method of Probability Plotting and continue 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
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 success 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 specifically, we start with the relatively simple method of Probability Plotting and continue 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
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 success 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 specifically, we start with the relatively simple method of Probability Plotting and continue 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
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 success 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 specifically, we start with the relatively simple method of Probability Plotting and continue 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
Template loop detected: Template:MLE Parameter Estimation
Template loop detected: Template:Bayesian Parameter Estimation Methods