Location family

A location family has distribution functions $F_{\theta }(x)=F_0(x-\theta )$, so changing the parameter translates the distribution without changing its shape.

The location score is the score function for translating a probability density function $f$. The minus sign comes from differentiating $f(y-\theta )$ in $\theta$. Under regular tail conditions, integration by parts gives $E_f\rho =0$ and $E_f(\epsilon \rho )=1$. These identities determine projections of the score function onto the span of the centered error.

For a known mean-zero noise law with finite variance ${\sigma }^2$, observing only $Y=a+ϵ$ with unrestricted $a\in \mathbb{R}$ has minimax risk ${\sigma }^2$ under squared-error loss. The observation itself attains this risk. For the lower bound, use a flat prior distribution on $[-A,A]$ and condition an easier experiment on $|ϵ|\le M$. Away from the prior endpoints, the Bayesian posterior noise has its original truncated law. Let $A\to \infty$ and then $M\to \infty$ in the resulting Bayes risk lower bound. No normal distribution or density for the noise is required.