Description Usage Arguments Details Value Examples
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
process with
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.
1  interv_fit(M, t_0 = 0, t_unit = 1, t_end = NULL)

M 
series of observed integrals 
t_0 
time at beginning of the first interval 
t_unit 
time span of each interval 
t_end 
time at the end of the last interval. If 
predict
A list of class interv_fit
, with the following attributes:
$data
: series M
$knots
: estimated value for the Wiener process in the points between
intervals
$sigma2
: estiamated scale parameter
$se
: standard errors for the knots values. They depend on sigma2 and on
homoskedasticity assumption
$covariances
: covariances of consecutive knots estimates.
1 2 3 4 5 6  M= c(0, 1, 1, 2, 4)
mod= interv_fit(M)
plot(mod)
mod= interv_fit(M, t_0= 5, t_unit= 2)
plot(mod)

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