JMP 12 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 13.2 Online Documentation
Basic Analysis
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Bivariate Analysis
• Robust
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Robust
The
Robust
option provides two methods to reduce the influence of outliers in your data set. Outliers can lead to incorrect estimates and decisions.
Fit Robust
The Fit Robust option reduces the influence of outliers in the response variable. The Huber M-estimation method is used. Huber M-estimation finds parameter estimates that minimize the Huber loss function, which penalizes outliers. The Huber loss function increases as a quadratic for small errors and linearly for large errors. For more details about robust fitting, see Huber (1973) and Huber and Ronchetti (2009).
Related Information
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Fitting Menus
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Example Using the Fit Robust Command
Fit Cauchy
Assumes that the errors have a Cauchy distribution. A Cauchy distribution has fatter tails than the normal distribution, resulting in a reduced emphasis on outliers. This option can be useful if you have a large proportion of outliers in your data. However, if your data are close to normal with only a few outliers, this option can lead to incorrect inferences. The Cauchy option estimates parameters using maximum likelihood and a Cauchy link function.