bregman1_sf: Bregman scoring function (type 1)

In scoringfunctions: A Collection of Loss Functions for Assessing Point Forecasts

Bregman scoring function (type 1)

Description

The function bregman1_sf computes the Bregman scoring function when y materialises and x is the predictive mean functional.

The Bregman scoring function is defined by eq. (18) in Gneiting (2011) and the form implemented here for \phi(x) = |x|^a is defined by eq. (19) in Gneiting (2011).

Usage

bregman1_sf(x, y, a)

Arguments

x	Predictive mean functional (prediction). It can be a vector of length n (must have the same length as y).
y	Realisation (true value) of process. It can be a vector of length n (must have the same length as x).
a	It can be a vector of length n (must have the same length as y).

Details

The Bregman scoring function (type 1) is defined by:

S(x, y, a) := |y|^a - |x|^a - a \textnormal{sign}(x) |x|^{a - 1} (y - x)

Domain of function:

x \in \mathbb{R}

y \in \mathbb{R}

a > 1

Range of function:

S(x, y, a) \geq 0, \forall x, y \in \mathbb{R}, a > 1

Value

Vector of Bregman losses.

Note

The implemented function is denoted as type 1 since it corresponds to a specific type of \phi(x) of the general form of the Bregman scoring function defined by eq. (18) in Gneiting (2011).

For details on the Bregman scoring function, see Savage (1971), Banerjee et al. (2005) and Gneiting (2011).

The mean functional is the mean \textnormal{E}_F[Y] of the probability distribution F of y (Gneiting 2011).

The Bregman scoring function is negatively oriented (i.e. the smaller, the better).

The herein implemented Bregman scoring function is strictly \mathbb{F}-consistent for the mean functional. \mathbb{F} is the family of probability distributions for which \textnormal{E}_F[Y] and \textnormal{E}_F[|Y|^a] exist and are finite (Savage 1971; Gneiting 2011).

References

Banerjee A, Guo X, Wang H (2005) On the optimality of conditional expectation as a Bregman predictor. IEEE Transactions on Information Theory 51(7):2664–2669. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TIT.2005.850145")}.

Gneiting T (2011) Making and evaluating point forecasts. Journal of the American Statistical Association 106(494):746–762. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/jasa.2011.r10138")}.

Savage LJ (1971) Elicitation of personal probabilities and expectations. Journal of the American Statistical Association 66(337):783–810. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.1971.10482346")}.

Examples

# Compute the Bregman scoring function (type 1).

df <- data.frame(
    y = rep(x = 0, times = 7),
    x = c(-3, -2, -1, 0, 1, 2, 3),
    a = rep(x = 3, times = 7)
)

df$bregman1_penalty <- bregman1_sf(x = df$x, y = df$y, a = df$a)

print(df)

# Equivalence of Bregman scoring function (type 1) and squared error scoring
# function, when a = 2.

set.seed(12345)

n <- 100

x <- runif(n, -20, 20)
y <- runif(n, -20, 20)
a <- rep(x = 2, times = n)

u <- bregman1_sf(x = x, y = y, a = a)

v <- serr_sf(x = x, y = y)

max(abs(u - v)) # values are slightly higher than 0 due to rounding error
min(abs(u - v))

scoringfunctions documentation built on April 4, 2025, 12:28 a.m.