StepSignalMargiLike (version 2.6.0)

est.mean.pois: est.mean.pois

Description

This function estimates the posterior mean for each segments under the Poisson assumption with conjugate prior. The data is assumed to follow Poisson(\(\lambda\)), where \(\lambda\) is assumed to have Beta prior with shape parameters \(\alpha\) and \(\beta\).

Usage

est.mean.pois(data.x, index.ChPT, prior)

Arguments

data.x

Observed data in vector form where each element represents a single observation.

index.ChPT

The set of the index of change points in a vector. Must be in accending order. This could be obtained by est.changepoints.

prior

Vector contatining prior parameters in the order of (\(\alpha, \beta\))

Value

Vector containing estimated mean for each segments.

Details

See Manual.pdf in "data" folder.

References

Chao Du, Chu-Lan Michael Kao and S. C. Kou (2015), "Stepwise Signal Extraction via Marginal Likelihood". Forthcoming in Journal of American Statistical Association.

Examples

# NOT RUN {
library(StepSignalMargiLike)

n <- 20
data.x <- rpois(n, 1)
data.x <- c(data.x, rpois(n, 10))
data.x <- c(data.x, rpois(n, 50))
data.x <- c(data.x, rpois(n, 20))
data.x <- c(data.x, rpois(n, 80))
data.x <- matrix(data.x,1)

prior <- c(1,2)
index.ChangePTs <- c(n, 2*n, 3*n, 4*n)
est.mean.pois(data.x, index.ChangePTs, prior)
# }