Normal approximation to normal

In my previous post on approximating a logistic distribution with a normal distribution I accidentally said something about approximating a normal with a normal.

Obviously the best approximation to a probability distribution is itself. As Norbert Wiener said The best material model of a cat is another, or preferably the same, cat."

But this made me think of the following problem. Let f be the density function of a standard normal random variable, i.e. one with mean zero and standard deviation 1. Let g be the density function of a normal random variable with mean > 0 and standard deviation .

For what value of does g best approximate f? Is it simply = 1? Does it depend on ?

I looked at the 1, 2, and norms, and in each case the optimal value of is not 1, and the optimal value does depend on . When is close to 0, is close to 1, as you'd probably expect. But for larger the results are surprising.

For the 1-norm and 2-norm, the optimal value of increases with and reaches a maximum of 2, then remains constant.

For the norm, the optimal value of increases briefly then decreases.

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