Maximum Likelihood interpolation of a Wiener process, given a series of observed integrals
over adiacent time intervals of equal lenght. To get point estimates of the interpolating
function, use method
predict. The method assumes a
homegeneous variance, but the estimated interpolating line is quite robust to
heteroskedasticity, anyway, also scale parameter
sigma2 is estimated as well as
SEs based on it.
series of observed integrals
time at beginning of the first interval
time span of each interval
time at the end of the last interval. If
A list of class
interv_fit, with the following attributes:
$data: series M
$knots: estimated value for the Wiener process in the points between
$sigma2: estiamated scale parameter
$se: standard errors for the knots values. They depend on sigma2 and on
$covariances: covariances of consecutive knots estimates.
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