Answers are grounded in this paper and cite their sources.
This note explores conditional distribution properties in hidden truncation models using Hermite polynomials, indicating implications for statistical analysis.
Let ( X , Y ) denote a two-dimensional random vector with a standard bivariate normal distribution. Here we consider the case in which X is unobserved and Y is only observed if X takes values in some specified interval. Then the distribution of the observed values of Y is the conditional distribution of Y given that X falls in that interval. In this note, the use of Hermite polynomials in analyzing the distributional properties of such models is considered. A general expression for the conditional expectation of polynomial functions of Y given that X is in some specified interval is given; this result can be used to find the conditional moments of Y . An expansion of the conditional expectation of a function of Y in terms of Hermite polynomials is derived and this result is used to derive expansions for the conditional distribution function of Y , for the distribution function of the skew-normal distribution and for Owen’s T-function.
No takes yet. Share an insight, caveat, or question.
Thomas A. Severini (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: