Fit a one-arm Dirichlet process mixture with a spliced GPD tail

dpmgpd() is the one-step convenience wrapper for the spliced bulk-tail model. It combines bundle and mcmc for one-arm data.

Usage

dpmgpd(
  y = NULL,
  X = NULL,
  treat = NULL,
  data = NULL,
  mcmc = list(),
  formula = NULL,
  ...
)

Arguments

y

Either a response vector or a bundle object.

X

Optional design matrix/data.frame.

treat

Optional binary treatment indicator. If supplied, this wrapper errors; use dpmgpd.causal() for causal models.

data

Optional data.frame used with formula.

mcmc

Named list of run arguments passed to mcmc() (including optional performance controls such as parallel_chains, workers, timing, and z_update_every).

formula

Optional formula.

...

Additional build arguments passed to build_nimble_bundle.

Value

A fitted object of class "mixgpd_fit".

Details

This wrapper targets the posterior predictive distribution obtained by combining a flexible bulk DPM with a generalized Pareto exceedance model above the threshold \(u(x)\). In the tail region the predictive density is proportional to $$\{1 - p_u(x)\} f_{\mathrm{GPD}}(y \mid x), \qquad y > u(x),$$ where \(p_u(x)\) is the posterior bulk mass below the threshold.

Use this wrapper when upper-tail behavior matters for inference, prediction, or extrapolation of extreme quantiles and survival probabilities.

See also

build_nimble_bundle, bundle, dpmix, predict.mixgpd_fit, summary.mixgpd_fit.