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added ref to regression

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mhjensen
2019-12-18 16:32:14 +01:00
parent 8c4db31d18
commit dae13921f1
13 changed files with 201 additions and 1141 deletions
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doc/pub/Regression/html/._Regression-bs000.html
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@@ -425,7 +425,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 13, 2019</h4></center> <!-- date -->
<center><h4>Dec 18, 2019</h4></center> <!-- date -->
<br>
<p>
doc/pub/Regression/html/._Regression-bs039.html
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@@ -437,7 +437,7 @@ with \( t \) a finite positive number.
<p>
We see that Ridge regression is nothing but the standard
OLS with a modified diagonal term added to \( \boldsymbol{X}^T\boldsymbol{X} \). The
consequences, in particular for our discussion of the bias-variance
consequences, in particular for our discussion of the bias-variance tradeoff
are rather interesting.
<p>
doc/pub/Regression/html/._Regression-bs097.html
+3
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@@ -437,6 +437,9 @@ sets. However, if a method has high variance then small changes in
the training data can result in large changes in the model. In general, more
flexible statistical methods have higher variance.
<p>
You may also find this recent <a href="https://www.pnas.org/content/116/32/15849" target="_self">article</a> of interest.
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
doc/pub/Regression/html/Regression-bs.html
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@@ -425,7 +425,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 13, 2019</h4></center> <!-- date -->
<center><h4>Dec 18, 2019</h4></center> <!-- date -->
<br>
<p>
doc/pub/Regression/html/Regression-reveal.html
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Sep 13, 2019</h4></center> <!-- date -->
<center><h4>Dec 18, 2019</h4></center> <!-- date -->
<br>
<p>
@@ -1919,7 +1919,7 @@ with \( t \) a finite positive number.
<p>
We see that Ridge regression is nothing but the standard
OLS with a modified diagonal term added to \( \boldsymbol{X}^T\boldsymbol{X} \). The
consequences, in particular for our discussion of the bias-variance
consequences, in particular for our discussion of the bias-variance tradeoff
are rather interesting.
<p>
@@ -3928,6 +3928,9 @@ estimate for our model should not vary too much between training
sets. However, if a method has high variance then small changes in
the training data can result in large changes in the model. In general, more
flexible statistical methods have higher variance.
<p>
You may also find this recent <a href="https://www.pnas.org/content/116/32/15849" target="_blank">article</a> of interest.
</section>
doc/pub/Regression/html/Regression-solarized.html
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@@ -305,7 +305,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 13, 2019</h4></center> <!-- date -->
<center><h4>Dec 18, 2019</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1946,7 +1946,7 @@ with \( t \) a finite positive number.
<p>
We see that Ridge regression is nothing but the standard
OLS with a modified diagonal term added to \( \boldsymbol{X}^T\boldsymbol{X} \). The
consequences, in particular for our discussion of the bias-variance
consequences, in particular for our discussion of the bias-variance tradeoff
are rather interesting.
<p>
@@ -3840,6 +3840,9 @@ sets. However, if a method has high variance then small changes in
the training data can result in large changes in the model. In general, more
flexible statistical methods have higher variance.
<p>
You may also find this recent <a href="https://www.pnas.org/content/116/32/15849" target="_blank">article</a> of interest.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/Regression/html/Regression.html
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@@ -310,7 +310,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 13, 2019</h4></center> <!-- date -->
<center><h4>Dec 18, 2019</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1951,7 +1951,7 @@ with \( t \) a finite positive number.
<p>
We see that Ridge regression is nothing but the standard
OLS with a modified diagonal term added to \( \boldsymbol{X}^T\boldsymbol{X} \). The
consequences, in particular for our discussion of the bias-variance
consequences, in particular for our discussion of the bias-variance tradeoff
are rather interesting.
<p>
@@ -3845,6 +3845,9 @@ sets. However, if a method has high variance then small changes in
the training data can result in large changes in the model. In general, more
flexible statistical methods have higher variance.
<p>
You may also find this recent <a href="https://www.pnas.org/content/116/32/15849" target="_blank">article</a> of interest.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>