weightedEuL {smoothemplik} | R Documentation |
Multi-variate Euclidean likelihood with analytical solution
Description
Multi-variate Euclidean likelihood with analytical solution
Usage
weightedEuL(
z,
mu = NULL,
ct = NULL,
vt = NULL,
shift = NULL,
SEL = TRUE,
weight.tolerance = NULL,
trunc.to = 0,
return.weights = FALSE,
verbose = FALSE,
chull.diag = FALSE
)
Arguments
z |
Numeric data vector. |
mu |
Hypothesized mean of |
ct |
Numeric count variable with non-negative values that indicates the multiplicity of observations.
Can be fractional. Very small counts below the threshold |
vt |
Numeric vector: non-negative variance weights for estimating the conditional
variance of |
shift |
The value to add in the denominator (useful in case there are extra Lagrange multipliers): 1 + lambda'Z + shift. |
SEL |
If |
weight.tolerance |
Weight tolerance for counts to improve numerical stability (similar to the ones in Art B. Owen's 2017 code, but adapting to the sample size). |
trunc.to |
Counts under |
return.weights |
Logical: if TRUE, individual EL weights are computed and returned. Setting this to FALSE gives huge memory savings in large data sets, especially when smoothing is used. |
verbose |
Logical: if |
chull.diag |
Logical: if The arguments
,
where This estimator is numerically equivalent to the Sieve Minimum Distance estimator
of (Ai and Chen 2003) with kernel sieves, but this interface
provides more flexibility through the two sets of weights. If |
Value
A list with the same structure as that in [weightedEL()].
References
Ai C, Chen X (2003).
“Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions.”
Econometrica, 71(6), 1795–1843.
ISSN 1468-0262, doi:10.1111/1468-0262.00470.
Owen AB (2001).
Empirical Likelihood.
Chapman and Hall/CRC, New York, USA.
See Also
[weightedEL()]
Examples
set.seed(1)
z <- cbind(rnorm(10), runif(10))
colMeans(z)
a <- weightedEuL(z, return.weights = TRUE)
a$wts
sum(a$wts) # Unity
colSums(a$wts * z) # Zero