The quantity to be estimated is \hat p = F(x'\hat b) where x is fixed and
\hat b is approximately N(b, V). If F were linear, then E(\hat p) =
F(x'E(b)). If F were convex, then E(\hat p) > F(x'E(b)), by Jensen's
inequality, etc.







Quoting Martin Elff <[log in to unmask]>:

> On Monday 30 January 2006 14:24, Franzese, Robert wrote:
> > This struck us as problematic; given Jensen's inequality, it would
> seem,
> > if we are correct, that this would be accurate,
>
> Dear all,
> educate me if I am wrong - why is Jensen's inequality relevant here?
> Inlow's suggestion does not involve the function of an integral.
> If anything gets transformed (a function is applied to it), it is the
> solution
> to an equation (which involves the unknown upper limit of an integral -
> see
> the TeX stuff below).
> But who would do a logit or probit transform to tail probabilities
> anyway?
>
> Best,
>
> Martin
>
>

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