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java.lang.Objectcom.numericalmethod.suanshu.stats.regression.linear.ols.OlsRegression
public class OlsRegression
(Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model. This method minimizes the (weighted) sum of squared distances between the observed responses in the dataset, and the responses predicted by the linear approximation.
The OLS estimator is consistent when the regressors are exogenous and there is no multicollinearity, and optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated. OLS can be derived as a maximum likelihood estimator under the assumption that the errors are normally distributed, however the method has good statistical properties for a much broader class of distributions (except for efficiency).
| Field Summary | |
|---|---|
Beta |
beta
the β^ statistics |
Diagnostics |
diagnostics
the diagnostic measures of this linear regression |
InformationCriteria |
informationCriteria
the model selection criteria |
LmProblem |
problem
the ordinary linear regression problem to be solved |
Residuals |
residuals
the residual analysis of this OLS regression |
| Constructor Summary | |
|---|---|
OlsRegression(LmProblem problem)
Construct an OlsRegression instance. |
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| Method Summary |
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| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
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public final LmProblem problem
public final Beta beta
public final Residuals residuals
public final Diagnostics diagnostics
public final InformationCriteria informationCriteria
| Constructor Detail |
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public OlsRegression(LmProblem problem)
problem - the linear regression problem to be solved
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SuanShu, a Java numerical and statistical library | |||||||
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