Manakah yang terbesar, dari sekumpulan variabel acak yang terdistribusi normal?

Saya memiliki variabel acak $X_0,X_1,\dots,X_n$ . $X_0$ memiliki distribusi normal dengan rata-rata $\mu>0$ dan varian $1$ . The $X_1,\dots,X_n$ rvs terdistribusi normal dengan rata-rata $0$ dan varians $1$ . Semuanya saling independen.

Misalkan $E$ menunjukkan peristiwa bahwa $X_0$ adalah yang terbesar dari ini, yaitu, $X_0 > \max(X_1,\dots,X_n)$ . Saya ingin menghitung atau memperkirakan$\Pr[E]$. I'm looking for an expression for $\Pr[E]$, as a function of $\mu,n$, or a reasonable estimate or approximation for $\Pr[E]$.

In my application, $n$ is fixed ($n=61$) and I want to find the smallest value for $\mu$ that makes $\Pr[E] \ge 0.99$, but I'm curious about the general question as well.

The calculation of such probabilities has been studied extensively by communications engineers under the name $M$-ary orthogonal signaling where the model is that one of $M$ equal-energy equally likely orthogonal signals being transmitted and the receiver attempting to decide which one was transmitted by examining the outputs of $M$ filters matched to the signals. Conditioned on the identity of the transmitted signal, the sample outputs of the matched filters are (conditionally) independent unit-variance normal random variables. The sample output of the filter matched to the signal transmitted is a $N(\mu,1)$ random variable while the outputs of all the other filters are $N(0,1)$ random variables.

The conditional probability of a correct decision (which in the present context is the event $C = \{X_0 > \max_i X_i\}$) conditioned on $X_0 = \alpha$ is

$$P(C \mid X_0 = \alpha) = \prod_{i=1}^n P\{X_i < \alpha \mid X_0 = \alpha\} = \left[\Phi(\alpha)\right]^n$$ where $\Phi(\cdot)$ is the cumulative probability distribution of a standard normal random variable, and hence the unconditional probability is $$P(C) = \int_{-\infty}^{\infty}P(C \mid X_0 = \alpha) \phi(\alpha-\mu)\,\mathrm d\alpha = \int_{-\infty}^{\infty}\left[\Phi(\alpha)\right]^n \phi(\alpha-\mu)\,\mathrm d\alpha$$ where $\phi(\cdot)$ is the standard normal density function. There is no closed-form expression for the value of this integral which must be evaluated numerically. Engineers are also interested in the complementary event -- that the decision is in error -- but do not like to compute this as $$P\{X_0 < \max_i X_i\} = P(E) = 1-P(C)$$ because this requires very careful evaluation of the integral for $P(C)$ to an accuracy of many significant digits, and such evaluation is both difficult and time-consuming. Instead, the integral for $1-P(C)$ can be integrated by parts to get $$P\{X_0 < \max_i X_i\} = \int_{-\infty}^{\infty} n \left[\Phi(\alpha)\right]^{n-1}\phi(\alpha) \Phi(\alpha - \mu)\,\mathrm d\alpha.$$ This integral is more easy to evaluate numerically, and its value as a function of $\mu$ is graphed and tabulated (though unfortunately only for $n \leq 20$) in Chapter 5 of Telecommunication Systems Engineering by Lindsey and Simon, Prentice-Hall 1973, Dover Press 1991. Alternatively, engineers use the union bound or Bonferroni inequality $$\begin{align*} P\{X_0 < \max_i X_i\} &= P\left\{(X_0 < X_1)\cup (X_0 < X_2) \cup \cdots \cup (X_0 < X_n)\right\}\\ &\leq \sum_{i=1}^{n}P\{X_0 < X_i\}\\ &= nQ\left(\frac{\mu}{\sqrt{2}}\right) \end{align*}$$ where $Q(x) = 1-\Phi(x)$ is the complementary cumulative normal distribution function.

From the union bound, we see that the desired value $0.01$ for $P\{X_0 < \max_i X_i\}$ is bounded above by $60\cdot Q(\mu/\sqrt{2})$ which bound has value $0.01$ at $\mu = 5.09\ldots$. This is slightly larger than the more exact value $\mu = 4.919\ldots$ obtained by @whuber by numerical integration.

More discussion and details about $M$-ary orthogonal signaling can be found on pp. 161-179 of my lecture notes for a class on communication systems'

A formal answer:

The probability distribution (density) for the maximum of $N$ i.i.d. variates is: $p_N(x)= N p(x) \Phi^{N-1}(x)$ where $p$ is the probability density and $\Phi$ is the cumulative distribution function.

From this you can calculate the probability that $X_0$ is greater than the $N-1$ other ones via $P(E) = (N-1) \int_{-\infty}^{\infty} \int_y^{\infty} p(x_0) p(y) \Phi^{N-2}(y) dx_0 dy$

You may need to look into various approximations in order to tractably deal with this for your specific application.