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Iosif Pinelis
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Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis, \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where \begin{equation*} G:=\min_{z>0}g(z)=g(z_*)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n), \end{equation*} \begin{equation*} z_*:=\left(\frac{n}{m}\right)^{\frac{m n}{2 (m+n)}}, \end{equation*} and
\begin{equation*} A:=\frac2{\pi mn}\,\sqrt{2\pi}\, \frac{z_*^{1/n - 1/m - 1}}{\sqrt{g''(z_*)/2}} =\frac2{\sqrt{\pi(m+n)}} \, m^{-\frac{m}{2 (m+n)}} n^{-\frac{n}{2 (m+n)}}. \end{equation*} Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Moreover, the roles of $m$ and $n$ on the right-hand side of (2) are interchangeable, as they should be. Furthermore, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).


Here is the graph of the ratio of $f_X(x)$ to the right-hand side of (2) for $m=3$, $n=4$, and $x\in[1,200]$:

enter image description here

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis, \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where \begin{equation*} G:=\min_{z>0}g(z)=g(z_*)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n), \end{equation*} \begin{equation*} z_*:=\left(\frac{n}{m}\right)^{\frac{m n}{2 (m+n)}}, \end{equation*} and
\begin{equation*} A:=\frac2{\pi mn}\,\sqrt{2\pi}\, \frac{z_*^{1/n - 1/m - 1}}{\sqrt{g''(z_*)/2}} =\frac2{\sqrt{\pi(m+n)}} \, m^{-\frac{m}{2 (m+n)}} n^{-\frac{n}{2 (m+n)}}. \end{equation*} Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Moreover, the roles of $m$ and $n$ on the right-hand side of (2) are interchangeable, as they should be. Furthermore, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis, \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where \begin{equation*} G:=\min_{z>0}g(z)=g(z_*)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n), \end{equation*} \begin{equation*} z_*:=\left(\frac{n}{m}\right)^{\frac{m n}{2 (m+n)}}, \end{equation*} and
\begin{equation*} A:=\frac2{\pi mn}\,\sqrt{2\pi}\, \frac{z_*^{1/n - 1/m - 1}}{\sqrt{g''(z_*)/2}} =\frac2{\sqrt{\pi(m+n)}} \, m^{-\frac{m}{2 (m+n)}} n^{-\frac{n}{2 (m+n)}}. \end{equation*} Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Moreover, the roles of $m$ and $n$ on the right-hand side of (2) are interchangeable, as they should be. Furthermore, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).


Here is the graph of the ratio of $f_X(x)$ to the right-hand side of (2) for $m=3$, $n=4$, and $x\in[1,200]$:

enter image description here

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Iosif Pinelis
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  • 107
  • 229

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis (cf. e.g. Theorem 3.4 (Watson's Lemma (Real version))standard asymptotic analysis, \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where $A$ is a certain positive real constant depending only on $m$ and $n$, \begin{equation*} G:=\min_{z>0}g(z)=g(z_*)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n), \end{equation*} \begin{equation*} z_*:=\left(\frac{n}{m}\right)^{\frac{m n}{2 (m+n)}}, \end{equation*} and
\begin{equation*} G:=\min_{z>0}g(z)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n). \end{equation*}

Thus\begin{equation*} A:=\frac2{\pi mn}\,\sqrt{2\pi}\, \frac{z_*^{1/n - 1/m - 1}}{\sqrt{g''(z_*)/2}} =\frac2{\sqrt{\pi(m+n)}} \, m^{-\frac{m}{2 (m+n)}} n^{-\frac{n}{2 (m+n)}}. \end{equation*} Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. AlsoMoreover, the roles of $m$ and $n$ on the right-hand side of (2) are interchangeable, as they should be. Furthermore, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis (cf. e.g. Theorem 3.4 (Watson's Lemma (Real version)), \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where $A$ is a certain positive real constant depending only on $m$ and $n$,
\begin{equation*} G:=\min_{z>0}g(z)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n). \end{equation*}

Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Also, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis, \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where \begin{equation*} G:=\min_{z>0}g(z)=g(z_*)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n), \end{equation*} \begin{equation*} z_*:=\left(\frac{n}{m}\right)^{\frac{m n}{2 (m+n)}}, \end{equation*} and
\begin{equation*} A:=\frac2{\pi mn}\,\sqrt{2\pi}\, \frac{z_*^{1/n - 1/m - 1}}{\sqrt{g''(z_*)/2}} =\frac2{\sqrt{\pi(m+n)}} \, m^{-\frac{m}{2 (m+n)}} n^{-\frac{n}{2 (m+n)}}. \end{equation*} Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Moreover, the roles of $m$ and $n$ on the right-hand side of (2) are interchangeable, as they should be. Furthermore, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

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Iosif Pinelis
  • 127.7k
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  • 107
  • 229

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis (cf. e.g. Theorem 3.4 (Watson's Lemma (Real version)), \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where $A$ is a certain positive real constant depending only on $m$ and $n$,
\begin{equation*} G:=\min_{z>0}g(z)=\frac{ m+n}{m}\,\left(\frac m n\right)^{n/(m+n)}. \end{equation*}\begin{equation*} G:=\min_{z>0}g(z)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n). \end{equation*}

Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Also, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis (cf. e.g. Theorem 3.4 (Watson's Lemma (Real version)), \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where $A$ is a certain positive real constant depending only on $m$ and $n$,
\begin{equation*} G:=\min_{z>0}g(z)=\frac{ m+n}{m}\,\left(\frac m n\right)^{n/(m+n)}. \end{equation*}

Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Also, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

Let $Z_i:=\nu_i$, $m:=e_1$, and $n:=e_2$. We want to find a bound on the tails of the pdf $f_X$ of the random variable (r.v.) \begin{equation*} X:=UV, \end{equation*} where \begin{equation*} U:=|Z_1|^m,\quad V:=|Z_2|^n. \end{equation*} Indeed, if $m$ and $n$ are both even, then $X=Y:=Z_1^m Z_2^n$, and otherwise $X=|Y|$ and the r.v. $Y$ is symmetric, so that $f_Y(y)=f_X(y)1(y>0)/2+f_X(-y)1(y\le0)/2$ for all real $y$, where $f_Y$ is the pdf of $Y$.

The pdf $f_X$ is the multiplicative convolution of the pdf $f_U$ and $f_V$ of the positive r.v.'s $U$ and $V$: for $x>0$, \begin{equation*} f_X(x)=\int_0^\infty\frac{dy}y\, f_U(x/y)f_V(y). \end{equation*} Also, \begin{equation*} f_U(u)=\frac2m u^{1/m-1}f(u^{1/m}),\quad f_V(v)=\frac2n v^{1/n-1}f(v^{1/n}) \tag{0} \end{equation*} for positive $u,v$, where $f$ is the pdf of $N(0,1)$. So, for $x>0$, \begin{equation*} f_X(x)=\frac2{\pi mn}\int_0^\infty dy\,x^{1/m-1}y^{1/n-1/m-1}\exp\{-[(x/y)^{2/m}+y^{2/n}]/2\}. \end{equation*} Making here the substitution $y=x^{an/2}z$, where \begin{equation*} a:=\frac2{m+n}, \tag{1} \end{equation*} we have \begin{equation*} f_X(x)=\frac2{\pi mn}\,x^b\int_0^\infty dz\,z^{1/n-1/m-1}\exp\{-x^a g(z)/2\}, \end{equation*} where $b:=1/m-1+(1/n-1/m)an/2=a-1$ and \begin{equation*} g(z):=z^{-2/m}+z^{2/n}. \end{equation*} So, by standard asymptotic analysis (cf. e.g. Theorem 3.4 (Watson's Lemma (Real version)), \begin{equation*} f_X(x)\sim A\,x^{a/2-1}\exp\{-Gx^a/2\} \tag{2} \end{equation*} as $x\to\infty$, where $A$ is a certain positive real constant depending only on $m$ and $n$,
\begin{equation*} G:=\min_{z>0}g(z)=m^{-\frac{m}{m+n}} n^{-\frac{n}{m+n}} (m+n). \end{equation*}

Thus, the exact exponent $a$ of $x$ in the term $\exp\{-Gx^a/2\}$ in (2) is given by (1). (This exponent is better than the corresponding exponent you had in the upper bound on the tail probability.)


Note that (2) will hold for all real $m,n>0$. That the exact exponent $a$ depends only on the total $m+n$ of the exponents of $Z_1$ and $Z_2$ in $Z_1^m Z_2^n$ seems quite reasonable. Also, in the limit cases when $m\downarrow0$ or $n\downarrow0$, (2) is in agreement with (0).

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