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Aug 16, 2022 at 23:01 vote accept RiXaTorAgu
Aug 15, 2022 at 1:17 comment added Sextus Empiricus I think I made a mistake in the comment and should use a minus $$ \int_\mathbb{R} t(z) e^{-(z-\mu)^2/2}\,\mathrm{d}z = \int_0^\infty \left(t(z) e^{-(z-\mu)^2/2}-t(-z) e^{-(-z-\mu)^2/2}\right)\,\mathrm{d}z$$ and end up with $$\int_0^\infty \left( g(z) e^{\mu z}-g(-z) e^{-\mu z} \right) \,\mathrm{d}z + \int_0^\infty \left( g(z) - g(-z) \right) e^{-\mu^2/2} \,\mathrm{d}z$$ but that is still not the same.
Aug 15, 2022 at 1:11 comment added whuber @Sextus I don't obtain that additional term. Please note that $e^{-\mu^2/2}$ is a factor of the original integral and therefore must be a factor of whatever you come up with.
Aug 15, 2022 at 1:09 comment added Sextus Empiricus I have this additional term $\int_0^\infty \left( g(z) + g(-z) \right) e^{-\mu^2/2} \,\mathrm{d}z $ is this integral $\int_\mathbb{R} g(z) \,\mathrm{d}z$ equal to zero?
Aug 15, 2022 at 1:08 comment added Sextus Empiricus $$ \int_\mathbb{R} t(z) e^{-(z-\mu)^2/2}\,\mathrm{d}z = \int_0^\infty \left(t(z) e^{-(z-\mu)^2/2}+t(-z) e^{-(-z-\mu)^2/2}\right)\,\mathrm{d}z = \int_0^\infty \left( t(z) e^{-(z^2+\mu^2-2\mu z)/2}+t(-z) e^{-(z^2+\mu^2+2\mu z)^2/2}\right)\,\mathrm{d}z = \int_0^\infty \left( g(z) e^{-\mu^2/2+\mu z}+g(-z) e^{-\mu^2/2-\mu z} \right) \,\mathrm{d}z = \int_0^\infty \left( g(z) e^{-\mu^2/2+\mu z}+g(-z) e^{-\mu^2/2-\mu z} \right) \,\mathrm{d}z = \int_0^\infty \left( g(z) e^{\mu z}+g(-z) e^{-\mu z} \right) \,\mathrm{d}z + \int_0^\infty \left( g(z) + g(-z) \right) e^{-\mu^2/2} \,\mathrm{d}z $$
Aug 15, 2022 at 0:50 comment added whuber @Sextus (1) Rao-Blackwellization, (2) Split into positive and negative parts and change the variable in the negative part.
Aug 14, 2022 at 18:52 comment added Sextus Empiricus The change with the integral domain from $\int_\mathbb{R}$ to $\int_0^\infty$ is not so clear.
Aug 14, 2022 at 18:42 comment added Sextus Empiricus This is a nice answer, but at the points where it states that an unbiased estimator must be a function of the sufficient statistic it goes too fast for me. It seems intuitive, but is there also some theorem or question/proof that states that an unbiased estimator does not exist if we can not construct an unbiased estimator based on the sufficient statistic?
Aug 14, 2022 at 17:38 comment added mlofton wow. amazing and thanks because I was wondering how one could obtain the correct estimator but had no clue.
Aug 14, 2022 at 16:09 history answered whuber CC BY-SA 4.0