Exact endpoint limit for a shifted exponential distribution ($n\lambda (Y_{(1)}-\theta )\sim \mathrm{Exp}(1)$)

For independent observations $Y_i=\theta +E_i$ with $E_i$ having exponential distribution of known rate $\lambda >0$, the likelihood increases in $\theta$ until the smallest observation, so the maximum-likelihood estimator is $Y_{(1)}$. Its survival function is $\mathbb{P}(Y_{(1)}-\theta >t)=e^{-n\lambda t}$ for $t\ge 0$. Therefore $n\lambda (Y_{(1)}-\theta )$ has exactly the unit-rate exponential distribution for every sample size. This gives statistical consistency and a one-sided nonnormal limit with rate $n$, illustrating how a support endpoint changes usual regular likelihood asymptotics.