Joint Change Point Detection 2.0
“Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null…”
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SHA-256eca734481182c095b021a13a2deed83ead9abcaa1fff8a3de1fa6dc32b6c5ed7
MaleculeH(Cm)Md
Evidence
1#' summary.jcp
2#'
3#' Summary method for class 'jcp'
4#'
5#' @param object object of class jcp
6#' @param ... additional arguments
7#'
8#' @r …
10:3… @examples
11#' #' # Normal distributed sequence with 3 change points at
12#' # c1=250 (change in expectation),
13#' # c2=500 (change in variance) and
14#' # c3=750 (change in expectation and variance)
15#' set.seed(0)
16#' m <- c(8,10,10,3); s <- c(4 …
2:2… '
3#' Joint change point detection - expectation and variance - via bivariate moving sum statistics
4#'
5#' @param x numeric vector. Input sequence o …
6:110… must an increasing vector of positive integers with maximum =< length(x)/2.
7#' @param q NA or numeric value. Rejection threshold. If NA (default), then the rejection boundary is derived in simulations (from Gaussian process limit) according to sim and …
137:39… all h and all t (list, i-th element is a matrix that corresponds to the i-th window)
138EVrho_list <- lapply(as.matrix(H),FUN=EVrho__matrix__fixed_h_de …
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