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For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

EDIT2 Going the a) route, could someone comment if this is rigorous enough: Suppose $\tilde{c}=np+c^*$, where $0<c^*\leq o(n^{1/6})$. Then \begin{align*} \frac{P(X=\tilde{c})}{P(X\geq\tilde{c})}&=\frac{P(X=np+c^*)}{P(X\geq np+c^*)}\\ &=\frac{(npq)^{-1/2}\phi(c^*/\sqrt{npq})(1+o(1))}{\phi(c^*/\sqrt{npq})/(c^*/\sqrt{npq})}\\ &=\frac{c^*}{npq}=o(1) \end{align*}

For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

EDIT2 Going the a) route, could someone comment if this is rigorous enough: Suppose $\tilde{c}=np+c^*$, where $0<c^*\leq o(n^{1/6})$. Then \begin{align*} \frac{P(X=\tilde{c})}{P(X\geq\tilde{c})}&=\frac{P(X=np+c^*)}{P(X\geq np+c^*)}\\ &=\frac{(npq)^{-1/2}\phi(c^*/\sqrt{npq})(1+o(1))}{\phi(c^*/\sqrt{npq})/(c^*/\sqrt{npq})}\\ &=\frac{c^*}{npq}=o(1) \end{align*}

For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

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For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

EDIT2 Going the a) route, could someone comment if this is rigorous enough: Suppose $\tilde{c}=np+c^*$, where $0<c^*\leq o(n^{1/6})$. Then \begin{align*} \frac{P(X=\tilde{c})}{P(X\geq\tilde{c})}&=\frac{P(X=np+c^*)}{P(X\geq np+c^*)}\\ &=\frac{(npq)^{-1/2}\phi(c^*/\sqrt{npq})(1+o(1))}{\phi(c^*/\sqrt{npq})/(c^*/\sqrt{npq})}\\ &=\frac{c^*}{npq}=o(1) \end{align*}

For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

EDIT2 Going the a) route, could someone comment if this is rigorous enough: Suppose $\tilde{c}=np+c^*$, where $0<c^*\leq o(n^{1/6})$. Then \begin{align*} \frac{P(X=\tilde{c})}{P(X\geq\tilde{c})}&=\frac{P(X=np+c^*)}{P(X\geq np+c^*)}\\ &=\frac{(npq)^{-1/2}\phi(c^*/\sqrt{npq})(1+o(1))}{\phi(c^*/\sqrt{npq})/(c^*/\sqrt{npq})}\\ &=\frac{c^*}{npq}=o(1) \end{align*}

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For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

EDIT2 Going the a) route, could someone comment if this is rigorous enough: Suppose $\tilde{c}=np+c^*$, where $0<c^*\leq o(n^{1/6})$. Then \begin{align*} \frac{P(X=\tilde{c})}{P(X\geq\tilde{c})}&=\frac{P(X=np+c^*)}{P(X\geq np+c^*)}\\ &=\frac{(npq)^{-1/2}\phi(c^*/\sqrt{npq})(1+o(1))}{\phi(c^*/\sqrt{npq})/(c^*/\sqrt{npq})}\\ &=\frac{c^*}{npq}=o(1) \end{align*}

For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

EDIT2 Going the a) route, could someone comment if this is rigorous enough: Suppose $\tilde{c}=np+c^*$, where $0<c^*\leq o(n^{1/6})$. Then \begin{align*} \frac{P(X=\tilde{c})}{P(X\geq\tilde{c})}&=\frac{P(X=np+c^*)}{P(X\geq np+c^*)}\\ &=\frac{(npq)^{-1/2}\phi(c^*/\sqrt{npq})(1+o(1))}{\phi(c^*/\sqrt{npq})/(c^*/\sqrt{npq})}\\ &=\frac{c^*}{npq}=o(1) \end{align*}

For a Binomial$(n,p)$ random variable $X$, I'm interested in showing that $$ \frac{P(X>c)}{P(X>c-1)}=1-o(1) $$ uniformly in $c\in\mathcal{R}$, where $\mathcal{R}$ is the range of interest (Note that $c$ will vary with $n$). The $o(1)$ rate is meant as $n\to\infty$.

Now I have the following results (Note that $q=1-p$):

Result 1 For $0\leq k\leq n$, set $$ P(X=k)=\frac{1}{\sqrt{2\pi pq n}}\exp\left(-\frac{(k-np)^2}{2npq} \right)(1+\delta_n(k)) $$ Then for every positive real sequence $\{c_n\}$ approaching zero, $$ \lim_{n\to\infty}\max_{k:|k-np|<c_n n^{2/3}}|\delta_n(k)|=0 $$

Result 2 Suppose that $\{a_n\}$ is a sequence of real numbers such that $\lim_{n\to\infty}a_n=+\infty$ and $\lim_{n\to\infty}a_n n^{-1/6}=0$. Then $$ P(X\geq np+a_n\sqrt{npq})\sim \frac{1}{a_n\sqrt{2\pi}}\exp(-a_n^2/2) $$ where "$\sim$" means asymptotic equivalence.


Now, \begin{align} \frac{P(X>c)}{P(X>c-1)}&=\frac{P(X>c-1)-P(X=c)}{P(X>c-1)}\\ &=1-\frac{P(X=c)}{P(X\geq c)} \end{align}

EDIT From the the above results, the range $\mathcal{R}$ can be at least $np$ and at most $np+c_{n}\sqrt{npq}$, for some $c_n=o(n^{1/6})$. I can certainly show that at the extremes of the range, the ratio is $o(1)$. However, I think I also need to show that either a) for some value $\tilde{c}$ in between the extremes, $P(X=\tilde{c})/P(X\geq \tilde{c})=o(1)$ or that b) the ratio itself is monotonic (based on some numerical experiments, I think it is increasing in $c$). I've tried to go through the route of b) and show that $P(X=c)/P(X\geq c)\leq P(X=c+1)/P(X\geq c+1)$, but can't seem to get the math to work out.

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Martin Sleziak
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