138 lines
4.0 KiB
TeX
138 lines
4.0 KiB
TeX
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\section{Generalized Linear Model}
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\[
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g(\EE(Y)) = X \beta
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\]
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with $g$ being
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\begin{itemize}
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\item Logistic regression: $g(v) = \log \left(\frac{v}{1-v}\right)$, for instance for boolean values,
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\item Poission regression: $g(v) = \log(v)$, for instance for discrete variables.
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\end{itemize}
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\subsection{Penalized Regression}
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When the number of variables is large, e.g, when the number of explanatory variable is above the number of observations, if $p >> n$ ($p$: the number of explanatory variable, $n$ is the number of observations), we cannot estimate the parameters.
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In order to estimate the parameters, we can use penalties (additional terms).
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Lasso regression, Elastic Net, etc.
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\subsection{Simple Linear Model}
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\begin{align*}
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\Y &= \X \beta + \varepsilon \\
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\begin{pmatrix}
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Y_1 \\
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Y_2 \\
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\vdots \\
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Y_n
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\end{pmatrix}
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&= \begin{pmatrix}
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1 & X_1 \\
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1 & X_2 \\
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\vdots & \vdots \\
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1 & X_n
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\end{pmatrix}
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\begin{pmatrix}
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\beta_0 \\
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\beta_1
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\end{pmatrix}
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+
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\begin{pmatrix}
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\varepsilon_1 \\
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\varepsilon_2 \\
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\vdots \\
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\varepsilon_n
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\end{pmatrix}
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\end{align*}
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\subsection{Assumptions}
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\begin{itemize}
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\item
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\end{itemize}
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\subsection{Statistical Analysis Workflow}
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\begin{enumerate}[label={\bfseries\color{primary}Step \arabic*.}]
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\item Graphical representation;
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\item ...
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\end{enumerate}
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\[
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Y = X \beta + \varepsilon,
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\]
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is noted equivalently as
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\[
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\begin{pmatrix}
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y_1 \\
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y_2 \\
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y_3 \\
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y_4
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\end{pmatrix}
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= \begin{pmatrix}
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1 & x_{11} & x_{12} \\
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1 & x_{21} & x_{22} \\
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1 & x_{31} & x_{32} \\
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1 & x_{41} & x_{42}
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\end{pmatrix}
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\begin{pmatrix}
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\beta_0 \\
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\beta_1 \\
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\beta_2
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\end{pmatrix} +
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\begin{pmatrix}
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\varepsilon_1 \\
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\varepsilon_2 \\
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\varepsilon_3 \\
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\varepsilon_4
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\end{pmatrix}.
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\]
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\section{Parameter Estimation}
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\subsection{Simple Linear Regression}
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\subsection{General Case}
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If $\X^\T\X$ is invertible, the OLS estimator is:
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\begin{equation}
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\hat{\beta} = (\X^\T\X)^{-1} \X^\T \Y
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\end{equation}
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\subsection{Ordinary Least Square Algorithm}
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We want to minimize the distance between $\X\beta$ and $\Y$:
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\[
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\min \norm{\Y - \X\beta}^2
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\]
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(See \autoref{ch:elements-of-linear-algebra}).
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\begin{align*}
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\Rightarrow& \X \beta = proj^{(1, \X)} \Y\\
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\Rightarrow& \forall v \in w,\, vy = v proj^w(y)\\
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\Rightarrow& \forall i: \\
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& \X_i \Y = \X_i X\hat{\beta} \qquad \text{where $\hat{\beta}$ is the estimator of $\beta$} \\
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\Rightarrow& \X^\T \Y = \X^\T \X \hat{\beta} \\
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\Rightarrow& {\color{gray}(\X^\T \X)^{-1}} \X^\T \Y = {\color{gray}(\X^\T \X)^{-1}} (\X^\T\X) \hat{\beta} \\
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\Rightarrow& \hat{\beta} = (X^\T\X)^{-1} \X^\T \Y
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\end{align*}
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This formula comes from the orthogonal projection of $\Y$ on the subspace define by the explanatory variables $\X$
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$\X \hat{\beta}$ is the closest point to $\Y$ in the subspace generated by $\X$.
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If $H$ is the projection matrix of the subspace generated by $\X$, $X\Y$ is the projection on $\Y$ on this subspace, that corresponds to $\X\hat{\beta}$.
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\section{Coefficient of Determination: \texorpdfstring{$R^2$}{R\textsuperscript{2}}}
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\begin{definition}[$R^2$]
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\[
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0 \leq R^2 = \frac{\norm{\X\hat{\beta} - \bar{\Y}\One}^2}{\norm{\Y - \bar{\Y}\One}^2} = 1 - \frac{\norm{\Y - \X\hat{\beta}}^2}{\norm{\Y - \bar{\Y}\One}^2} \leq 1
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\] proportion of variation of $\Y$ explained by the model.
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\end{definition}
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\begin{figure}
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\centering
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\includestandalone{figures/schemes/orthogonal_projection}
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\caption{Orthogonal projection of $\Y$ on plan generated by the base described by $\X$. $\color{blue}a$ corresponds to $\norm{\X\hat{\beta} - \bar{\Y}}^2$ and $\color{blue}b$ corresponds to $\norm{\Y - \hat{\beta}\X}^2$}
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\label{fig:scheme-orthogonal-projection}
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\end{figure} |