How to calculate $\int_{-\infty}^{\infty} \Phi(y+c)^{k-1}d(\Phi(y))$ How to calculate $\int_{-\infty}^{\infty} \Phi(y+c)^{k-1}d(\Phi(y))$?When I check the list of integrals of Gaussian functions, I only find k-1=2.
 A: Let $X_0, X_1, \cdots, X_{k-1}$ denote $k$ independent unit-variance normal random variables, and suppose that $E[X_0]=0$, and $E[X_i]=-c$, $1 \leq i \leq k-1$. Let $C$ denote the event that $X_0 > \max_i X_i$. Then,
$$P(C\mid X_0 = y) = P(\max_i X_i \leq y) = \prod_{i=1}^{k-1}P(X_i \leq y) = [\Phi(y+c)]^{k-1}.$$
Consequently,
$$P(C) = \int_{-\infty}^\infty [\Phi(y+c)]^{k-1}\,\mathrm d[\Phi(y)]
= \int_{-\infty}^\infty [\Phi(y+c)]^{k-1}\phi(y)\,\mathrm dy$$
where $\phi(y)$ is the standard normal density function.  As whuber says in a comment, there is no closed-form expression for the value of this integral but it is nicely behaved and amenable to numerical evaluation. Evaluating integrals of this type is of great interest to communications engineers -- more truthfully, accurate evaluation of $1-P(C)$ is of great interest to communications engineers-- because $1-P(C)$ corresponds to the probability of error in orthogonal signaling.  See, for example, this answer of mine for some details.
