The lifetime in hours of each bulb manufactured by a particular company follows an independent exponential distribution with mean $ \theta $ . To test the hypothesis $ \ H_0 : 2000 $ versus $ \ H_1 : 1000 $ an experimenter sets up an experiment with 50 bulbs with 5 bulbs in each of 10 different locations to examine their lifetimes. To get quick preliminary results, the experimenter decides to stop the experiment as soon as one bulb fails at each location. Let Y, denote the lifetime of the first bulb to fail at location i, i = 1, 2, ..., 10.
Obtain the most powerful test of size 0.05 to test $ \ H_0 $, versus $ \ H_1 $ based on the available lifetimes.
My attempt with the help of the hint provided
Since $ \ Y_i = min(\ X_1,\ X_2 ,\ X_3 ,\ X_4,\ X_5) $
$ P(min(X_1,...,X_p) < t) = 1 - P(min(X_1,...,X_p) > t)= 1 - P(X_1 > t, ..., X_p > t) $
$ 1 - (1 - P(X_1 < t) \times ... \times (1 - P(X_p < t)) = 1 - \left(1 - P(X_i < t)\right)^p $
After this $ \ P(X_i < t) = \int_0^t e^-\theta d\theta $
$ \ 1- \ e^-t $ Now if I put this value of cdf in above equation
$ 1 - \left(1 - 1+ \ e^-t \right)^p $
$ 1 - e^-{p\theta} $
Now if I differentiate this cdf to obtain the pdf of $\ Y_i $ then it is $ exp(-p\theta) $
Here $ p = 5 $ So $\ Y_i $ ~ $ exp(-5\theta) $
$ \sum_{i=0}^{10} \ Y_i $ ~ $gamma(10,5\theta ) $