com.numericalmethod.suanshu.stats.regression.linear.ols

Class OLSRegression

java.lang.Object

com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression

All Implemented Interfaces:

java.io.Serializable

public class OLSRegression
extends java.lang.Object
implements java.io.Serializable

(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).

See Also:

• Wikipedia: Linear regression
• Wikipedia: Least squares
• Wikipedia: Ordinary least squares
  , Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
Beta	beta the \(\hat{\beta}\) 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

Constructors

Constructor and Description
OLSRegression(LMProblem problem) Construct an OLSRegression instance.

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

problem

public final LMProblem problem

the ordinary linear regression problem to be solved

beta

public final Beta beta

the \(\hat{\beta}\) statistics

residuals

public final Residuals residuals

the residual analysis of this OLS regression

diagnostics

public final Diagnostics diagnostics

the diagnostic measures of this linear regression

informationCriteria

public final InformationCriteria informationCriteria

the model selection criteria

Constructor Detail

OLSRegression

public OLSRegression(LMProblem problem)

Construct an OLSRegression instance.

Parameters:

problem - the linear regression problem to be solved