est.mean.norm: est.mean.norm

In StepSignalMargiLike: Step-Wise Signal Extraction via Marginal Likelihood

Description

This function estimates the posterior mean for each segments under the normal assumption with conjugate prior. The variance σ^2 is assumed to be drawn from an inverse Gamma distribution with shape parameter ν0 and scale parameter σ0^2, while mean is assumed to be drawn from a normal distribution with mean μ0 and variance σ^2/κ0.

Usage

est.mean.norm(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 (μ0, κ0, ν0, σ0^2).

Details

See Manual.pdf in "data" folder.

Value

Vector containing estimated mean for each segments.

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

library(StepSignalMargiLike)

n <- 5
data.x <- rnorm(n, 1, 1)
data.x <- c(data.x, rnorm(n, 10,1))
data.x <- c(data.x, rnorm(n, 2,1))
data.x <- c(data.x, rnorm(n, 10,1))
data.x <- c(data.x, rnorm(n, 1,1))

prior <- prior.norm.A(data.x)
index.ChPT <- c(n,2*n,3*n,4*n)
est.mean.norm(data.x, index.ChPT, prior)

Example output

[1] 1.123217 9.406438 2.612413 9.549323 1.581905

StepSignalMargiLike documentation built on May 2, 2019, 1:02 p.m.