typo
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@@ -649,7 +649,7 @@ which leads to
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$$
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\hat{\boldsymbol{\beta}}_i^{\mathrm{Lasso}} = \left\{\begin{array}{ccc}y_i-\frac{\lambda}{2} &\mathrm{if} & y_i> \frac{\lambda}{2}\\
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y_i+\frac{\lambda}{2} &\mathrm{if} & y_i < -\frac{\lambda}{2}\\
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0 &\mathrm{if} & \verty_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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0 &\mathrm{if} & \vert y_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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$$
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<p> <br>
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@@ -666,7 +666,7 @@ which leads to
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$$
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\hat{\boldsymbol{\beta}}_i^{\mathrm{Lasso}} = \left\{\begin{array}{ccc}y_i-\frac{\lambda}{2} &\mathrm{if} & y_i> \frac{\lambda}{2}\\
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y_i+\frac{\lambda}{2} &\mathrm{if} & y_i < -\frac{\lambda}{2}\\
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0 &\mathrm{if} & \verty_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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0 &\mathrm{if} & \vert y_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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$$
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<p>
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@@ -671,7 +671,7 @@ which leads to
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$$
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\hat{\boldsymbol{\beta}}_i^{\mathrm{Lasso}} = \left\{\begin{array}{ccc}y_i-\frac{\lambda}{2} &\mathrm{if} & y_i> \frac{\lambda}{2}\\
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y_i+\frac{\lambda}{2} &\mathrm{if} & y_i < -\frac{\lambda}{2}\\
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0 &\mathrm{if} & \verty_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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0 &\mathrm{if} & \vert y_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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$$
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<p>
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Binary file not shown.
@@ -764,7 +764,7 @@
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"$$\n",
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"\\hat{\\boldsymbol{\\beta}}_i^{\\mathrm{Lasso}} = \\left\\{\\begin{array}{ccc}y_i-\\frac{\\lambda}{2} &\\mathrm{if} & y_i> \\frac{\\lambda}{2}\\\\\n",
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" y_i+\\frac{\\lambda}{2} &\\mathrm{if} & y_i< -\\frac{\\lambda}{2}\\\\\n",
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"\t\t\t\t\t\t\t 0 &\\mathrm{if} & \\verty_i\\vert\\le \\frac{\\lambda}{2}\\end{array}\\right.\\\\.\n",
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"\t\t\t\t\t\t\t 0 &\\mathrm{if} & \\vert y_i\\vert\\le \\frac{\\lambda}{2}\\end{array}\\right.\\\\.\n",
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"$$"
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]
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},
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@@ -778,6 +778,7 @@
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"We will amongst other things show that the regularization parameter can reduce considerably the variance of the parameters $\\beta$.\n",
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"\n",
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"\n",
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"\n",
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"<!-- !split -->\n",
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"## Linking the regression analysis with a statistical interpretation\n",
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"\n",
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@@ -419,7 +419,7 @@ which leads to
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\[
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\hat{\bm{\beta}}_i^{\mathrm{Lasso}} = \left\{\begin{array}{ccc}y_i-\frac{\lambda}{2} &\mathrm{if} & y_i> \frac{\lambda}{2}\\
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y_i+\frac{\lambda}{2} &\mathrm{if} & y_i< -\frac{\lambda}{2}\\
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0 &\mathrm{if} & \verty_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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0 &\mathrm{if} & \vert y_i\vert\le \frac{\lambda}{2}\end{array}\right.\\.
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\]
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!et
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@@ -429,6 +429,7 @@ We will now couple the discussions of ordinary least squares, Ridge and Lasso re
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We will amongst other things show that the regularization parameter can reduce considerably the variance of the parameters $\beta$.
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!split
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===== Linking the regression analysis with a statistical interpretation =====
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