mv_if: Mean - variance identification function

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

Mean - variance identification function

Description

The function mv_if computes the mean - variance identification function, when y materialises, x_1 is the predictive mean and x_2 is the predictive variance.

The mean - variance identification function is defined in proposition (3.11) in Fissler and Ziegel (2019).

Usage

mv_if(x1, x2, y)

Arguments

x1	Predictive mean (prediction). It can be a vector of length n (must have the same length as y).
x2	Predictive variance (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_1).

Details

The mean - variance identification function is defined by:

V(x_1, x_2, y) := (x_1 - y, x_2 + x_1^2 - y^2)

Domain of function:

x_1 \in \mathbb{R}

x_2 > 0

y \in \mathbb{R}

Value

Matrix of mean - variance values of the identification function.

Note

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

The variance functional is the variance \textnormal{Var}_F[Y] := \textnormal{E}_F[Y^2] - (\textnormal{E}_F[Y])^{2} of the probability distribution F of y (Gneiting 2011)

The mean - variance identification function is a strict \mathbb{F}-identification function for the pair (mean, variance) functional (Gneiting 2011; Fissler and Ziegel 2019; Dimitriadis et al. 2024).

\mathbb{F} is the family of probability distributions F for which \textnormal{E}_F[Y] and \textnormal{E}_F[Y^2] exist and are finite (Gneiting 2011; Fissler and Ziegel 2019; Dimitriadis et al. 2024).

References

Dimitriadis T, Fissler T, Ziegel JF (2024) Osband's principle for identification functions. Statistical Papers 65:1125–1132. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00362-023-01428-x")}.

Fissler T, Ziegel JF (2019) Order-sensitivity and equivariance of scoring functions. Electronic Journal of Statistics 13(1):1166–1211. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/19-EJS1552")}.

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")}.

Examples

# Compute the mean - variance identification function.

df <- data.frame(
    y = rep(x = 0, times = 6),
    x1 = c(2, 2, -2, -2, 0, 0),
    x2 = c(1, 2, 1, 2, 1, 2)
)

v <- as.data.frame(mv_if(x1 = df$x1, x2 = df$x2, y = df$y))

print(cbind(df, v))

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