ppi_mmmm: A PPI Score-Matching Marginal Moment Matching Estimator...

In scorematchingad: Score Matching Estimation by Automatic Differentiation

A PPI Score-Matching Marginal Moment Matching Estimator (dimension=3 only)

Description

Computes a marginal moment matching estimator \insertCite@Section 6.2, @scealy2023scscorematchingad, which assumes \beta is a known vector with the same value in each element, and b_L = 0. Only A_L is estimated.

Usage

ppi_mmmm(Y, ni, beta0, w = rep(1, nrow(Y)))

Arguments

Y	Count data, each row is a multivariate observation.
ni	The total for each sample (sum across rows)
beta0	\beta=\beta_0 is the same for each component.
w	Weights for each observation. Useful for weighted estimation in Windham().

Details

\beta=\beta_0 is fixed and not estimated. b_L is fixed at zero. See \insertCite@Section 6.2 and A.8 of @scealy2023scscorematchingad. The boundary weight function in the score matching discrepancy is the unthresholded product weight function

h(z)^2 = \min\left(\prod_{j=1}^{p} z_j^2, a_c^2\right).

Value

A vector of estimates for A_L entries (diagonal and off diagonal).

References

\insertAllCited

scorematchingad documentation built on April 4, 2025, 12:15 a.m.