analistica/slides/sections/4.md

Sample statistics

Sample statistics

How to estimate sample median, mode and FWHM?

. . .

• \only<3>\strike{Binning data \hence depends wildly on bin-width}

. . .

• Alternative solutions
  • Robust estimators
  • Kernel density estimation

Sample median

:::: {.columns} ::: {.column width=50% .c}

$$
  F(m) = \frac{1}{2}

\vspace{20pt}

. . .

• Sort points in ascending order

. . .

• Middle element if odd

  Average of the two central elements if even :::

::: {.column width=50%} ::: ::::

Sample mode

Most probable value

. . .

Half Sample Mode

• Iteratively identify the smallest interval containing half points
• Once the sample is reduced to less than three points, take average

. . .

\setbeamercovered{}

\begin{center} \begin{tikzpicture}[remember picture] % line \draw [line width=3, ->, cyclamen] (-5,0) -- (5,0); \node [right] at (5,0) {$x$}; % points \draw [blue, fill=blue] (-4.6,-0.1) rectangle (-4.8,0.1); \draw [blue, fill=blue] (-4,-0.1) rectangle (-4.2,0.1); \draw [blue, fill=blue] (-3.3,-0.1) rectangle (-3.5,0.1); \draw [blue, fill=blue] (-2.3,-0.1) rectangle (-2.5,0.1); \draw [blue, fill=blue] (-0.6,-0.1) rectangle (-0.8,0.1); \draw [blue, fill=blue] (-0.1,-0.1) rectangle (0.1,0.1); \draw [blue, fill=blue] (1.1,-0.1) rectangle (1.3,0.1); \draw [blue, fill=blue] (2 ,-0.1) rectangle (2.2,0.1); \draw [blue, fill=blue] (2.7,-0.1) rectangle (2.9,0.1); \draw [blue, fill=blue] (4,-0.1) rectangle (4.2,0.1); % future nodes \node at (-1,-0.3) (1a) {}; \node at (3.1,0.3) (1b) {}; \node at (0.9,-0.3) (2a) {}; \node at (1.8,-0.3) (3a) {}; % result nodes \node at (2.45,-0.7) (f1) {}; \node at (2.45,0.7) (f2) {}; \end{tikzpicture} \end{center}

. . .

\begin{center} \begin{tikzpicture}[remember picture, overlay] % region \draw [orange, fill=orange, opacity=0.5] (1a) rectangle (1b); \end{tikzpicture} \end{center}

. . .

\begin{center} \begin{tikzpicture}[remember picture, overlay] % region \draw [orange, fill=orange, opacity=0.5] (2a) rectangle (1b); \end{tikzpicture} \end{center}

. . .

\begin{center} \begin{tikzpicture}[remember picture, overlay] % region \draw [orange, fill=orange, opacity=0.5] (3a) rectangle (1b); \end{tikzpicture} \end{center}

. . .

\begin{center} \begin{tikzpicture}[remember picture, overlay] % region \draw [cyclamen, ultra thick] (f1) -- (f2); \end{tikzpicture} \end{center}

Sample FWHM

  \text{FWHM} = x_+ - x_- \with L(x_{\pm}) = \frac{L_{\text{max}}}{2}

\setbeamercovered{transparent} . . .

Kernel Density Estimation

• empirical PDF construction:

  f_\varepsilon(x) = \frac{1}{N\varepsilon} \sum_{i = 1}^N
  G \left( \frac{x-x_i}{\varepsilon} \right)

The parameter \varepsilon controls the strength of the smoothing

Sample FWHM

Silverman's rule of thumb:

  f_\varepsilon(x) = \frac{1}{N\varepsilon} \sum_{i = 1}^N
  G \left( \frac{x-x_i}{\varepsilon} \right)
  \with
  \varepsilon = 0.88 \, S_N
  \left( \frac{d + 2}{4}N \right)^{-1/(d + 4)}

with:

• S_N is the sample standard deviation
• d is number of dimensions (d = 1)

. . .

Numerical minimization (Brent) for \quad f_{\varepsilon_{\text{max}}}
Numerical root finding (Brent) for $\quad f_{\varepsilon}(x_{\pm}) = \frac{f_{\varepsilon_{\text{max}}}}{2}$

Sample FWHM