mle.GRW: Analytical ML estimator for random walk and stasis models

In paleoTS: Analyze Paleontological Time-Series

Analytical ML estimator for random walk and stasis models

Description

Analytical ML estimator for random walk and stasis models

Usage

mle.GRW(y)

mle.URW(y)

mle.Stasis(y)

Arguments

y	a paleoTS object

Value

a vector of mstep and vstep for mle.GRW, vstep for mle.URW, and theta and omega for mle.Stasis

Functions

• mle.URW(): ML parameter estimates for URW model

• mle.Stasis(): ML parameter estimates for Stasis model

Note

These analytical solutions assume even spacing of samples and equal sampling variance in each, which will usually be violated in real data. They are used here mostly to generate initial parameter estimates for numerical optimization; they not likely to be called directly by the user.

See Also

fitSimple

Examples

y <- sim.GRW(ms = 1, vs = 1)
w <- mle.GRW(y)
print(w)

paleoTS documentation built on Sept. 11, 2024, 9:18 p.m.