\newpage
\begin{appendices}
  \counterwithin{lstfloat}{section}
  \section{Notes on Proofs of Lemmata in Section~\ref{sec:conv}}
  \label{appendix:proofs}
  Contrary to \textcite{heiss2019}  we do not make the distinction between $f_+$ and
  $f_-$.
  This results in some alterations in the proofs being necessary. In
  the following the affected proofs and the required changes are given.
  % Because of that slight alterations are needed in the proofs of
  % .. auxiliary lemmata.
  % Alterations that go beyond substituting $F_{+-}^{}$
  % As the proofs are ... for the most part only
  % the alterations needed are specified.

  

  
  % In the following there will be proofs for some important Lemmata in
  % Section~\ref{sec:theo38}. Further proofs not discussed here can be
  % found in \textcite{heiss2019}
  % The proves in this section are based on \textcite{heiss2019}. Slight
  % alterations have been made to accommodate for not splitting $f$ into
  % $f_+$ and $f_-$.
  % \begin{Theorem}[Proof of Lemma~\ref{theo38}]
  % \end{Theorem}
  
  % \begin{Lemma}[$\frac{w^{*,\tilde{\lambda}}_k}{v_k}\approx\mathcal{O}(\frac{1}{n})$]
  %   For any $\lambda > 0$ and training data $(x_i^{\text{train}},
  %   y_i^{\text{train}}) \in \mathbb{R}^2, \, i \in
  %   \left\{1,\dots,N\right\}$, we have
  %   \[
  %     \max_{k \in \left\{1,\dots,n\right\}} \frac{w^{*,
  %         \tilde{\lambda}}_k}{v_k} = \po_{n\to\infty}
  %   \]
  
    
  % \end{Lemma}

  \begin{Proof}[Heiss, Teichmann, and Wutte (2019, Lemma A.9)]~\\\noindent
    \label{proof:lem9}
    With $\tilde{\lambda} \coloneqq \lambda n g(0)$ Lemma~\ref{lem:cnvh} follows
    analogously when considering $\tilde{w}$, $f_g^{*, \lambda}$, and $h_k$
    instead of $\tilde{w}^+$, $f_{g,+}^{*, \lambda}$, and $\bar{h}_k$.
    Consider $\kappa = \left\{1, \dots, n \right\}$ for $n$ nodes
    instead of $\kappa^+$. With $h_k = \frac{1}{n g_\xi(\xi_n)}$
    instead of $\bar{h}_k$
    and \[
      \mathbb{E} \left[\abs{\left\{m \in \kappa : \xi_m \in [\delta l,
        \delta(l+1))\right\}}\right] = n \int_{\delta
    l}^{\delta(l+1)}g_\xi (x) dx \approx n (\delta g_\xi(\delta l)
  \pm \delta \tilde{\varepsilon}).
    \]
   %  \[
%   \sum_{k \in \kappa : \xi_k < T} \varphi(\xi_k, v_k)
%   h_{k,n} = \sum_{\substack{l \in \mathbb{Z} \\ [\delta l, \delta
%       (l+1)) \in [C_{g_\xi}^l,\min\{C_{g_\xi}^u, T \}]}}
%   \left(\sum_{\substack{k \in \kappa \\ \xi_k \in
%         [\delta l , \delta(l+1))}} \varphi(\xi_k, v_k) 
%     h_{k,n}\right) \approx
% \]
% \[
%   \approx \sum_{\substack{l \in \mathbb{Z} \\ [\delta l, \delta
%       (l+1)) \in [C_{g_\xi}^l,\min\{C_{g_\xi}^u, T \}]}}
%   \left(\sum_{\substack{k \in \kappa \\ \xi_k \in
%         [\delta l , \delta(l+1))}} \left(\varphi(\delta l, v_k)
%       \frac{1}{n g_\xi (\delta l)} \pm \frac{\varepsilon}{n}\right) 
%     \frac{\abs{\left\{m \in \kappa : \xi_m \in [\delta l,
%           \delta(l+1))\right\}}}{\abs{\left\{m \in \kappa : \xi_m
%           \in [\delta l, \delta(l+1))\right\}}}\right)
% \]
% \[
%   \approx \sum_{\substack{l \in \mathbb{Z} \\ [\delta l, \delta
%       (l+1)) \in [C_{g_\xi}^l,\min\{C_{g_\xi}^u, T \}]}}
%   \left(\frac{\sum_{\substack{k \in \kappa \\ \xi_k \in
%           [\delta l , \delta(l+1))}}\varphi(\delta l,
%       v_k)}{\abs{\left\{m \in \kappa : \xi_m
%           \in [\delta l, \delta(l+1))\right\}}} 
%     \frac{\abs{\left\{m \in \kappa : \xi_m \in [\delta l,
%           \delta(l+1))\right\}}}{n g_\xi (\delta l)}\right) \pm \varepsilon
% \]
% The amount of kinks in a given interval of length $\delta$ follows a
% binomial distribution,
% \[
%   \mathbb{E} \left[\abs{\left\{m \in \kappa : \xi_m \in [\delta l,
%         \delta(l+1))\right\}}\right] = n \int_{\delta
%     l}^{\delta(l+1)}g_\xi (x) dx \approx n (\delta g_\xi(\delta l)
%   \pm \delta \tilde{\varepsilon}),
% \]
% for any $\delta \leq \delta(\varepsilon, \tilde{\varepsilon})$, since $g_\xi$ is uniformly continuous on its
% support by Assumption..
% As the distribution of $v$ is continuous as well we get that
% $\mathcal{L}(v_k) = \mathcal{L} v| \xi = \delta l) \forall k \in
% \kappa : \xi_k \in [\delta l, \delta(l+1))$ for $\delta \leq
% \delta(\varepsilon, \tilde{\varepsilon})$. Thus we get with the law of
% large numbers
% \begin{align*}
%   &\sum_{k \in \kappa : \xi_k < T} \varphi(\xi_k, v_k)
%     h_{k,n} \approx\\  
%   &\approx \sum_{\substack{l \in \mathbb{Z} \\ [\delta l, \delta
%   (l+1)) \in [C_{g_\xi}^l,\min\{C_{g_\xi}^u, T
%   \}]}}\left(\mathbb{E}[\phi(\xi, v)|\xi=\delta l]
%   \stackrel{\mathbb{P}}{\pm}\right) \delta \left(1 \pm
%   \frac{\tilde{\varepsilon}}{g_\xi(\delta l)}\right) \pm \varepsilon
%   \\
%   &\approx \left(\sum_{\substack{l \in \mathbb{Z} \\ [\delta
%   l, \delta 
%   (l+1)) \in [C_{g_\xi}^l,\min\{C_{g_\xi}^u, T
%   \}]}}\mathbb{E}[\phi(\xi, v)|\xi=\delta l] \delta
%   \stackrel{\mathbb{P}}{\pm}\tilde{\tilde{\varepsilon}}
%   \abs{C_{g_\xi}^u - C_{g_\xi}^l}
%   \right)\\
%   &\phantom{\approx}\cdot \left(1 \pm
%     \frac{\tilde{\varepsilon}}{g_\xi(\delta l)}\right) \pm \varepsilon
% \end{align*}
\end{Proof}

% \begin{Lemma}[($L(f_n) \to L(f)$), Heiss, Teichmann, and
% Wutte (2019, Lemma A.11)]
%   For any data $(x_i^{\text{train}}, y_i^{\text{train}}) \in
%   \mathbb{R}^2, i \in \left\{1,\dots,N\right\}$, let $(f_n)_{n \in
%     \mathbb{N}}$ be a sequence of functions that converges point-wise
%   in probability to a function $f : \mathbb{R}\to\mathbb{R}$, then the
%   loss $L$ of $f_n$ converges is probability to $L(f)$ as $n$ tends to
%   infinity,
%   \[
%     \plimn L(f_n) = L(f).
%   \]
%   \proof Vgl. ...
% \end{Lemma}

\begin{Proof}[Heiss, Teichmann, and Wutte (2019, Lemma A.12)]~\\\noindent
    \label{proof:lem12}
   With $\tilde{\lambda} \coloneqq \lambda n g(0)$ Lemma~\ref{lem:s2} follows
    analogously when considering $\tilde{w}$, $f_g^{*, \lambda}$, and $h_k$
    instead of $\tilde{w}^+$, $f_{g,+}^{*, \lambda}$, and $\bar{h}_k$.
  % We start by showing that
  % \[
  %   \plimn \tilde{\lambda} \norm{\tilde{w}}_2^2 = \lambda g(0)
  %   \left(\int \frac{\left(f_g^{*,\lambda''}\right)^2}{g(x)} dx\right)
  % \]
  % With the definitions of $\tilde{w}$, $\tilde{\lambda}$ and
  % $h$ we have
  % \begin{align*}
  %   \tilde{\lambda} \norm{\tilde{w}}_2^2
  %   &= \tilde{\lambda} \sum_{k \in
  %     \kappa}\left(f_g^{*,\lambda''}(\xi_k) \frac{h_k
  %     v_k}{\mathbb{E}v^2|\xi = \xi_k]}\right)^2\\
  %   &= \tilde{\lambda} \sum_{k \in
  %     \kappa}\left(\left(f_g^{*,\lambda''}\right)^2(\xi_k) \frac{h_k
  %     v_k^2}{\mathbb{E}v^2|\xi = \xi_k]}\right) h_k\\
  %   & = \lambda g(0) \sum_{k \in
  %     \kappa}\left(\left(f_g^{*,\lambda''}\right)^2(\xi_k)\frac{v_k^2}{g_\xi(\xi_k)\mathbb{E}
  %     [v^2|\xi=\xi_k]}\right)h_k.
  % \end{align*}
  % By using Lemma~\ref{lem} with $\phi(x,y) =
  % \left(f_g^{*,\lambda''}\right)^2(x)\frac{y^2}{g_\xi(\xi)\mathbb{E}[v^2|\xi=y]}$
  % this converges to
  % \begin{align*}
  %   &\plimn \tilde{\lambda}\norm{\tilde{w}}_2^2 = \\
  %   &=\lambda
  %     g_\xi(0)\mathbb{E}[v^2|\xi=0]\int_{\supp{g_\xi}}\mathbb{E}\left[
  %     \left(f_g^{*,\lambda''}\right)^2(\xi)\frac{v^2}{
  %     g_\xi(\xi)\mathbb{E}[v^2|\xi=x]^2}\Big{|} \xi = x\right]dx\\
  %   &=\lambda g_\xi(0) \mathbb{E}[v^2|\xi=0] \int_{\supp{g_xi}}
  %     \frac{\left(f_g^{*,\lambda''}\right)^2 (x)}{g_\xi(x)
  %     \mathbb{E}[v^2|\xi=x]} dx \\
  %   &=\lambda g(0) \int_{\supp{g_\xi}} \frac{\left(f_g^{*,\lambda''}\right)^2}{g(x)}dx.
  % \end{align*}
\end{Proof}

\begin{Proof}[Heiss, Teichmann, and Wutte (2019, Lemma A.14)]~\\\noindent
    \label{proof:lem14}
  Substitute $F_{+-}^{\lambda, g}\left(f_{g,+}^{*,\lambda},
    f_{g,-}^{*,\lambda}\right)$ with $F^{\lambda,g}\left(f_g^{*,\lambda}\right)$.
\end{Proof}
% \begin{Lemma}[Heiss, Teichmann, and
%   Wutte (2019, Lemma A.13)]
%   Using the notation of Definition .. and ... the following statement
%   holds:
%   $\forall \varepsilon \in \mathbb{R}_{>0} : \exists \delta \in
%   \mathbb{R}_{>0} : \forall \omega \in \Omega : \forall l, l' \in
%   \left\{1,\dots,N\right\} : \forall n \in \mathbb{N}$
%   \[
%     \left(\abs{\xi_l(\omega) - \xi_{l'}(\omega)} < \delta \wedge
%       \text{sign}(v_l(\omega)) = \text{sign}(v_{l'}(\omega))\right)
%     \implies \abs{\frac{w_l^{*, \tilde{\lambda}}(\omega)}{v_l(\omega)}
%       - \frac{w_{l'}^{*, \tilde{\lambda}}(\omega)}{v_{l'}(\omega)}} <
%     \frac{\varepsilon}{n},
%   \]
%   if we assume that $v_k$ is never zero.
%   \proof given in ..
% \end{Lemma}

% \begin{Lemma}[$\frac{w^{*,\tilde{\lambda}}}{v} \approx
%     \mathcal{O}(\frac{1}{n})$, Heiss, Teichmann, and
%     Wutte (2019, Lemma A.14)]
%     For any $\lambda > 0$ and data $(x_i^{\text{train}},
%     y_i^{\text{train}}) \in \mathbb{R}^2, i\in
%     \left\{1,\dots,\right\}$, we have
%     \[
%       \forall P \in (0,1) : \exists C \in \mathbb{R}_{>0} : \exists
%       n_0 \in \mathbb{N} : \forall n > n_0 : \mathbb{P}
%       \left[\max_{k\in \left\{1,\dots,n\right\}}
%         \frac{w_k^{*,\tilde{\lambda}}}{v_k} < C
%         \frac{1}{n}\right] > P
%       % \max_{k\in \left\{1,\dots,n\right\}}
%       % \frac{w_k^{*,\tilde{\lambda}}}{v_k} = \plimn
%     \]
%     \proof
    
    
%     Let $k^*_+ \in \argmax_{k\in
%       \left\{1,\dots,n\right\}}\frac{w^{*,\tilde{\lambda}}}{v_k} : v_k
%     > 0$ and $k^*_- \in \argmax_{k\in
%       \left\{1,\dots,n\right\}}\frac{w^{*,\tilde{\lambda}}}{v_k} : v_k
%     < 0$. W.l.o.g. assume $\frac{w_{k_+^*}^2}{v_{k_+^*}^2} \geq
%     \frac{w_{k_-^*}^2}{v_{k_-^*}^2}$
%     \begin{align*}
%       \frac{F^{\lambda,
%       g}\left(f^{*,\lambda}_g\right)}{\tilde{\lambda}}
%       \makebox[2cm][c]{$\stackrel{\mathbb{P}}{\geq}$}
%       & \frac{1}{2 \tilde{\lambda}}
%         F_n^{\tilde{\lambda}}\left(\mathcal{RN}^{*,\tilde{\lambda}}\right)
%         = \frac{1}{2 \tilde{\lambda}}\left[\sum ... + \tilde{\lambda} \norm{w}_2^2\right]
%       \\
%       \makebox[2cm][c]{$\geq$}
%       & \frac{1}{2}\left( \sum_{\substack{k: v_k
%         > 0 \\\xi_k\in(\xi_{k^*}, \xi_{k^*}
%       + \delta)}} \left(w_k^{*,\tilde{\lambda}}\right)^2 +
%       \sum_{\substack{k: v_k < 0 \\\xi_k\in(\xi_{k^*}, \xi_{k^*}
%       + \delta)}} \left(w_k^{*,\tilde{\lambda}}\right)^2\right) \\
%       \makebox[2cm][c]{$\overset{\text{Lem. A.6}}{\underset{\delta \text{
%       small enough}}{\geq}} $}
%       &
%         \frac{1}{4}\left(\left(\frac{w_{k_+^*}^{*,\tilde{\lambda}}}
%         {v_{k_+^*}}\right)^2\sum_{\substack{k:
%         v_k > 0 \\\xi_k\in(\xi_{k^*}, \xi_{k^*} + \delta)}}v_k^2 +
%       \left(\frac{w_{k_-^*}^{*,\tilde{\lambda}}}{v_{k_-^*}}\right)^2
%       \sum_{\substack{k:  
%         v_k < 0 \\\xi_k\in(\xi_{k^*}, \xi_{k^*} +
%       \delta)}}v_k^2\right)\\
%       \makebox[2cm][c]{$\stackrel{\mathbb{P}}{\geq}$}
%       & \frac{1}{8}
%         \left(\frac{w_{k_+^*}^{*,\tilde{\lambda}}}{v_{k^*}}\right)^2
%         n \delta g_\xi(\xi_{k_+^*}) \mathbb{P}(v_k
%         >0)\mathbb{E}[v_k^2|\xi_k = \xi_{k^*_+}]
%     \end{align*}
  
%   \end{Lemma}

\begin{Proof}[Heiss, Teichmann, and Wutte (2019, Lemma A.15)]~\\\noindent
    \label{proof:lem15}
  Consider $\mathcal{RN}^{*,\tilde{\lambda}}$,
  $f^{w^{*,\tilde{\lambda}}}$, and $\kappa = \left\{1, \dots, n
  \right\}$ instead of $\mathcal{RN}_+^{*,\tilde{\lambda}}$,
  $f_+^{w^{*,\tilde{\lambda}}}$, and $\kappa^+$.
  Assuming w.l.o.g. $max_{k \in
    \kappa^+}\abs{\frac{w_k^{*,\tilde{\lambda}}}{v_k}} \geq max_{k \in
    \kappa^-}\abs{\frac{w_k^{*,\tilde{\lambda}}}{v_k}}$
  Lemma~ref{lem:s3} follows analogously by multiplying (58b) with two.
\end{Proof}

\begin{Proof}[Heiss, Teichmann, and Wutte (2019, Lemma
  A.16)]~\\\noindent
    \label{proof:lem16}
  As we are considering $F^{\lambda,g}$ instead of
  $F^{\lambda,g}_{+-}$ we need to substitute $2\lambda g(0)$ with
  $\lambda g(0)$
  and thus get
  \[
   \left(f^{w^{*,\tilde{\lambda}}}\right)''(x) \approx
    \frac{w_{l_x}^{*,\tilde{\lambda}}}{v_{l_x}} n g_\xi(x)
    \mathbb{E}\left[v_k^2|\xi_k = x\right] \stackrel{\mathbb{P}}{\pm} \varepsilon_3
  \]
  and use this to follow
  \[
    \lambda g(0)
    \int_{\supp(g)}\hspace{-0.15cm}\frac{\left(\left(f^{w^{*,\tilde{\lambda}}}\right)''(x)\right)^2}{g(0)}dx 
    \approx \tilde{\lambda} n
    \int_{\supp(g)}\left(\frac{w_{l_x}^{*,\tilde{\lambda}}}{v_{l_x}}\right)^2 \hspace{-0.1cm}
    g_xi(x) \mathbb{E}\left[v_k^2|\xi_k=x\right]dx
  \]
  Analogous to the proof of \textcite{heiss2019} we get
  \begin{align*}
    \tilde{\lambda} \sum_{k \in \kappa}
    \left(w_k^{*,\tilde{\lambda}}\right)^2
    &= \tilde{\lambda} \sum_{k \in \kappa^+}
      \left(w_k^{*,\tilde{\lambda}}\right)^2 + \tilde{\lambda} \sum_{k \in \kappa^-}
      \left(w_k^{*,\tilde{\lambda}}\right)^2 \\
    &\approx \left(\mathbb{P}[v_k <0] + \mathbb{P}[v_k >0]\right)\\
    &\phantom{=} 
      \int_{\supp(g_xi)}
      \left(\frac{w_{l_x}^{*,\tilde{\lambda}}}{v_{l_x}}\right)^2
      g_\xi(x) \mathbb{E}\left[v_k^2|\xi_k = x\right] dx
      \stackrel{\mathbb{P}}{\pm} \varepsilon_9 \\
    &= \int_{\supp{g_xi}}
      \left(\frac{w_{l_x}^{*,\tilde{\lambda}}}{v_{l_x}}\right)^2
      g_\xi(x) \mathbb{E}\left[v_k^2|\xi_k = x\right] dx
      \stackrel{\mathbb{P}}{\pm} \varepsilon_9.      
  \end{align*}
  With these transformations Lemma~\ref{lem:s4} follows analogously.
\end{Proof}

\begin{Proof}[Heiss, Teichmann, and Wutte (2019, Lemma A.19)]~\\\noindent
    \label{proof:lem19}
  The proof works analogously if $F_{+-}^{\lambda,g}$ is substituted
  by
  \begin{align*}
    F_{+-}^{\lambda,g '}(f_+, f_-) =
    & \sum_{i =
            1}^N \left(f(x_i^{\text{train}}) -
                                      y_i^{\text{train}}\right)^2 \\
    & + \lambda g(0) \left(\int_{\supp(g)}\frac{\left(f_+''(x)\right)^2}{g(x)}
        dx + \int_{\supp(g)}\frac{\left(f''_-(x)\right)^2}{g(x)}
        dx\right).
  \end{align*}
  As for $f^n = f_+^n + f_-^n$ such that $\supp(f_+^n) \cap \supp(f_-^n) =
  \emptyset$ and $h = h_+ + h_-$ such that $\supp(h_+) \cap \supp(h_-) =
  \emptyset$ it holds
  \[
    \plimn F^{\lambda, g}(f^n) = F^{\lambda, g}(h) \implies
    \plimn F_{+-}^{\lambda,g '}(f_+,f_-) = F_{+-}^{\lambda,g '}(h_+,h_-),
  \]
  and all functions can be split in two functions with disjoint support,
  Lemma~\ref{lem:s7} follows.
\end{Proof}
\input{Appendix_code.tex}

\end{appendices}






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