org.apache.commons.math3.stat.regression.MultipleLinearRegression Maven / Gradle / Ivy
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.stat.regression;
/**
* The multiple linear regression can be represented in matrix-notation.
*
* y=X*b+u
*
* where y is an n-vector regressand, X is a [n,k] matrix whose k columns are called
* regressors, b is k-vector of regression parameters and u is an n-vector
* of error terms or residuals.
*
* The notation is quite standard in literature,
* cf eg Davidson and MacKinnon, Econometrics Theory and Methods, 2004.
* @since 2.0
*/
public interface MultipleLinearRegression {
/**
* Estimates the regression parameters b.
*
* @return The [k,1] array representing b
*/
double[] estimateRegressionParameters();
/**
* Estimates the variance of the regression parameters, ie Var(b).
*
* @return The [k,k] array representing the variance of b
*/
double[][] estimateRegressionParametersVariance();
/**
* Estimates the residuals, ie u = y - X*b.
*
* @return The [n,1] array representing the residuals
*/
double[] estimateResiduals();
/**
* Returns the variance of the regressand, ie Var(y).
*
* @return The double representing the variance of y
*/
double estimateRegressandVariance();
/**
* Returns the standard errors of the regression parameters.
*
* @return standard errors of estimated regression parameters
*/
double[] estimateRegressionParametersStandardErrors();
}