PSPA Quantile Estimation

Helper function for PSPA quantile estimation

Usage

pspa_quantile(Y_l, f_l, f_u, q, weights = NA, alpha = 0.05)

Arguments

Y_l

(vector): n-vector of labeled outcomes.

f_l

(vector): n-vector of predictions in the labeled data.

f_u

(vector): N-vector of predictions in the unlabeled data.

q

(float): Quantile to estimate. Must be in the range (0, 1).

weights

(array): 1-dimensional array of weights vector for variance reduction. PSPA will estimate the weights if not specified.

alpha

(scalar): type I error rate for hypothesis testing - values in (0, 1); defaults to 0.05.

Value

A list of outputs: estimate of inference model parameters and corresponding standard error.

Details

Post-prediction adaptive inference (Miao et al. 2023) https://arxiv.org/abs/2311.14220

Examples

dat <- simdat(model = "quantile")

form <- Y - f ~ 1

Y_l <- dat[dat$set == "labeled",   all.vars(form)[1]] |> matrix(ncol = 1)

f_l <- dat[dat$set == "labeled",   all.vars(form)[2]] |> matrix(ncol = 1)

f_u <- dat[dat$set == "unlabeled", all.vars(form)[2]] |> matrix(ncol = 1)

pspa_quantile(Y_l, f_l, f_u, q = 0.5)
#> $est
#> [1] 0.9942712
#> 
#> $se
#> [1] 0.0652868
#>