View Javadoc
1   /*
2    * Licensed to the Apache Software Foundation (ASF) under one or more
3    * contributor license agreements.  See the NOTICE file distributed with
4    * this work for additional information regarding copyright ownership.
5    * The ASF licenses this file to You under the Apache License, Version 2.0
6    * (the "License"); you may not use this file except in compliance with
7    * the License.  You may obtain a copy of the License at
8    *
9    *      https://www.apache.org/licenses/LICENSE-2.0
10   *
11   * Unless required by applicable law or agreed to in writing, software
12   * distributed under the License is distributed on an "AS IS" BASIS,
13   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14   * See the License for the specific language governing permissions and
15   * limitations under the License.
16   */
17  
18  /*
19   * This is not the original file distributed by the Apache Software Foundation
20   * It has been modified by the Hipparchus project
21   */
22  package org.hipparchus.stat.regression;
23  
24  /**
25   * The multiple linear regression can be represented in matrix-notation.
26   * <pre>
27   *  y=X*b+u
28   * </pre>
29   * where y is an <code>n-vector</code> <b>regressand</b>, X is a <code>[n,k]</code> matrix whose <code>k</code> columns are called
30   * <b>regressors</b>, b is <code>k-vector</code> of <b>regression parameters</b> and <code>u</code> is an <code>n-vector</code>
31   * of <b>error terms</b> or <b>residuals</b>.
32   *
33   * The notation is quite standard in literature,
34   * cf eg <a href="http://www.econ.queensu.ca/ETM">Davidson and MacKinnon, Econometrics Theory and Methods, 2004</a>.
35   */
36  public interface MultipleLinearRegression {
37  
38      /**
39       * Estimates the regression parameters b.
40       *
41       * @return The [k,1] array representing b
42       */
43      double[] estimateRegressionParameters();
44  
45      /**
46       * Estimates the variance of the regression parameters, ie Var(b).
47       *
48       * @return The [k,k] array representing the variance of b
49       */
50      double[][] estimateRegressionParametersVariance();
51  
52      /**
53       * Estimates the residuals, ie u = y - X*b.
54       *
55       * @return The [n,1] array representing the residuals
56       */
57      double[] estimateResiduals();
58  
59      /**
60       * Returns the variance of the regressand, ie Var(y).
61       *
62       * @return The double representing the variance of y
63       */
64      double estimateRegressandVariance();
65  
66      /**
67       * Returns the standard errors of the regression parameters.
68       *
69       * @return standard errors of estimated regression parameters
70       */
71       double[] estimateRegressionParametersStandardErrors();
72  
73  }