ghuber_sf {scoringfunctions} | R Documentation |
Generalized Huber scoring function
Description
The function ghuber_sf computes the generalized Huber scoring function at a
specific level and parameters
and
, when
materializes and
is the predictive Huber functional at level
.
The generalized Huber scoring function is defined by eq. (4.7) in Taggart (2022)
for .
Usage
ghuber_sf(x, y, p, a, b)
Arguments
x |
Predictive Huber functional (prediction) at level |
y |
Realization (true value) of process. It can be a vector of length
|
p |
It can be a vector of length |
a |
It can be a vector of length |
b |
It can be a vector of length |
Details
The generalized Huber scoring function is defined by:
where is the capping function defined by:
Domain of function:
Range of function:
Value
Vector of generalized Huber losses.
Note
For the definition of Huber functionals, see definition 3.3 in Taggart (2022). The value of eq. (4.7) is twice the value of the equation in definition 4.2 in Taggart (2002).
The generalized Huber scoring function is negatively oriented (i.e. the smaller, the better).
The generalized Huber scoring function is strictly consistent for the
-Huber functional relative to the family
of potential
probability distributions
for the future
for which
and
exist and are finite
(Taggart 2022).
References
Taggart RJ (2022) Point forecasting and forecast evaluation with generalized Huber loss. Electronic Journal of Statistics 16:201–231. doi:10.1214/21-EJS1957.
Examples
# Compute the generalized Huber scoring function.
set.seed(12345)
n <- 10
df <- data.frame(
x = runif(n, -2, 2),
y = runif(n, -2, 2),
p = runif(n, 0, 1),
a = runif(n, 0, 1),
b = runif(n, 0, 1)
)
df$ghuber_penalty <- ghuber_sf(x = df$x, y = df$y, p = df$p, a = df$a, b = df$b)
print(df)
# Equivalence of the generalized Huber scoring function and the asymmetric
# piecewise quadratic scoring function (expectile scoring function), when
# a = Inf and b = Inf.
set.seed(12345)
n <- 100
x <- runif(n, -20, 20)
y <- runif(n, -20, 20)
p <- runif(n, 0, 1)
a <- rep(x = Inf, times = n)
b <- rep(x = Inf, times = n)
u <- ghuber_sf(x = x, y = y, p = p, a = a, b = b)
v <- expectile_sf(x = x, y = y, p = p)
max(abs(u - v)) # values are slightly higher than 0 due to rounding error
min(abs(u - v))
# Equivalence of the generalized Huber scoring function and the Huber scoring
# function when p = 1/2 and a = b.
set.seed(12345)
n <- 100
x <- runif(n, -20, 20)
y <- runif(n, -20, 20)
p <- rep(x = 1/2, times = n)
a <- runif(n, 0, 20)
u <- ghuber_sf(x = x, y = y, p = p, a = a, b = a)
v <- huber_sf(x = x, y = y, a = a)
max(abs(u - v)) # values are slightly higher than 0 due to rounding error
min(abs(u - v))