Given a matrix formed by \(M\) vectors (columns), measured at
\(T\) times (rows), M.record
calculates the number of records in
the \(M\) vectors at each observed time \(t\), \(S_t\).
The function p.record
is equivalent, but calculates the proportion
of records at each time \(t\), that is the ratio:
$$\hat p_t = \frac{S_t}{M} = \frac{I_{t,1} + \ldots + I_{t,M}}{M},$$
this proportion is an estimation of the probability of record at that time.
Following the notation in I.record
, in summary:
$$\code{X} = \left(
\begin{array}{cccc}
X_{1,1} & X_{1,2} & \cdots & X_{1,M} \\
X_{2,1} & X_{2,2} & \cdots & X_{2,M} \\
\vdots & \vdots & & \vdots \\
X_{T,1} & X_{T,2} & \cdots & X_{T,M} \\
\end{array} \right)
\begin{array}{lc}
\stackrel{\code{S.record}}{\longrightarrow} &
\Big( S_1, S_2, \cdots, S_T \Big) \\ \\
\stackrel{\code{p.record}}{\longrightarrow} &
\Big( \hat p_1, \hat p_2, \cdots, \hat p_T \Big) \\
\end{array}$$
Summaries for both upper and lower records can be calculated.