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Distributional transformations without orthogonality relations

Distributional transformations characterized by equations relating expectations of test functions weighted by a given biasing function on the original distribution to expectations of the test function's higher derivatives with respect to the transformed distribution play a great role in Stein's method and were, in great generality, first considered by Goldstein and Reinert \cite{GolRei05b}. We prove two abstract existence and uniqueness results for such distributional transformations, generalizing their $X-P$ bias transformation. On the one hand, we show how one can abandon previously necessary orthogonality relations by subtracting an explicitly known polynomial depending on the test function from the test function itself. On the other hand, we prove that for a given nonnegative integer $m$ it is possible to obtain the expectation of the $m$-th derivative of the test function with respect to the transformed distribution in the defining equation, even though the biasing function may have $k<m$ sign changes, if these two numbers have the same parity. We explain, how these results can be used to guarantee the existence of two different generalizations of the zero bias transformation by Goldstein and Reinert \cite{GolRei97}. Further applications include the derivation of Stein type characterizations without needing to solve any Stein equation and the presentation of a general framework of estimating the distance of the distribution of a given real random variable $X$ to that of a random variable $Z$, whose distribution is characterized by some $m$-th order linear differential operator. We also explain the fact that, in general, the biased distribution depends on the choice of the sign change points, if these are ambiguous. This new phenomenon does not appear in the framework from \cite{GolRei05b}.