analistica/slides/sections/5.md

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# Kolmogorov-Smirnov test
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## KS
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Quantify distance between expected and observed CDF. KS statistic:
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:::: {.columns}
::: {.column width=50% .c}
$$
D_N = \text{sup}_x |F_N(x) - F(x)|
$$
\vspace{20pt}
- $F(x)$ is the expected CDF
- $F_N(x)$ is the empirical CDF
- sort points in ascending order
- number of points preceding the point normalized by $N$
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. . .
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:::
::: {.column width=50%}
\setbeamercovered{}
\begin{center}
\begin{tikzpicture}[>=Stealth]
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% empiric
\draw [cyclamen, fill=cyclamen!20!white] (-2.5,0) rectangle (-1.5,0.5);
\draw [cyclamen, fill=cyclamen!20!white] (-1.5,0) rectangle (-0.9,1);
\draw [cyclamen, fill=cyclamen!20!white] (-0.9,0) rectangle (-0.6,1.5);
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\draw [cyclamen, fill=cyclamen!20!white] (-0.6,0) rectangle ( 0.5,2);
\draw [cyclamen, fill=cyclamen!20!white] ( 0.5,0) rectangle ( 0.7,2.5);
\draw [cyclamen, fill=cyclamen!20!white] ( 0.7,0) rectangle ( 1.2,3);
\draw [cyclamen, fill=cyclamen!20!white] ( 1.2,0) rectangle ( 1.6,3.5);
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\draw [cyclamen, fill=cyclamen!20!white] ( 1.6,0) rectangle ( 2.3,4);
\draw [cyclamen, fill=cyclamen!20!white] ( 2.3,0) rectangle ( 2.5,4.5);
% points
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\draw [yellow!50!black, fill=yellow] (-2.6,-0.1) rectangle (-2.4,0.1); %-2.5
\draw [yellow!50!black, fill=yellow] (-1.6,-0.1) rectangle (-1.4,0.1); %-1.5
\draw [yellow!50!black, fill=yellow] (-1,-0.1) rectangle (-0.8,0.1); %-0.9
\draw [yellow!50!black, fill=yellow] (-0.7,-0.1) rectangle (-0.5,0.1); %-0.6
\draw [yellow!50!black, fill=yellow] (0.4,-0.1) rectangle (0.6,0.1); % 0.5
\draw [yellow!50!black, fill=yellow] (0.6,-0.1) rectangle (0.8,0.1); % 0.7
\draw [yellow!50!black, fill=yellow] (1.1,-0.1) rectangle (1.3,0.1); % 1.2
\draw [yellow!50!black, fill=yellow] (1.5,-0.1) rectangle (1.7,0.1); % 1.6
\draw [yellow!50!black, fill=yellow] (2.2,-0.1) rectangle (2.4,0.1); % 2.3
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% expected
\pause
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\draw[domain=-2.5:2.5, yscale=5, smooth, variable=\x, yellow, very thick]
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plot ({\x}, {((atan(\x)*pi/180) + pi/2)/pi});
\pause
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\draw [very thick, cyclamen, <->] (0.5,2.5) -- (0.5,3.25);
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\end{tikzpicture}
\end{center}
\setbeamercovered{transparent}
:::
::::
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## KS
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$H_0$: points sampled according to $F(x)$
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. . .
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If $H_0$ is true: $\sqrt{N}D_N \xrightarrow{N \rightarrow + \infty} K$
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$K$ Kolmogorov variable with CDF:
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$$
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P(K \leqslant K_0) = \frac{\sqrt{2 \pi}}{K_0}
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\sum_{j = 1}^{+ \infty} e^{-(2j - 1)^2 \pi^2 / 8 K_0^2}
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$$
. . .
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A $p$-value can be computed
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- At 95% confidence level, $H_0$ cannot be disproved if $p > 0.05$