# Variance of alternate flipping rounds

I did the following exercise, but I would like to extend the question to the variance of the variate.

Bob and Bub each has his own coin. Chance of coming up "heads" is $\rho$ for Bob's coin and $\tau$ for Bub's. They flip alternatively, first Bob, then Bub, then Bob again, etc. Let Bob's flip followed by Bub's flip constitute a round, and let $R$ denote the number of rounds until each gets "heads" at least once. For $\rho = 1/3$, $\tau = 2/5$, what is the expectation of $R$?

General answer for the expectation is:

$$\mathbb{E}[R]=\frac{1 + \frac{\rho}{\tau} + \frac{\tau}{\rho} - (\rho + \tau)}{\rho + \tau - \rho \, \tau}$$

This agrees with Monte Carlo simulation I did (with $10^5$ repeats), which approximates expectation and variance (with $\rho = 1/3$, and $\tau = 2/5$) to $3.84647$ and $6.48666$ respectively.

Is anybody able to calculate variance symbolically?

• Out of curiosity, where does your expected value formula come from?
– Remy
Dec 1, 2017 at 0:23
• @Remy I derived it. In a nutshell, $R$ is distributed geometrically with parameter $p = \rho + \tau - \rho \, \tau$ (probability of success of either one of them) plus a geometric variate with parameter $p = \rho$ and weighted with probability $\mathbb{P}\{\text{Bob didn't have success, but Bub did} \, | \, \text{either had}\}$ plus a similar variate for the reverse case.
– BoLe
Dec 2, 2017 at 12:51

Please load the page $$2\sim3$$ times for the hyperlinks to work properly.

# Outline

Setup the Notations : As titled.

Solution.1 : Direct application of the conditional decomposition of expectation. This is the foolproof approach if one wants a quick numeric evaluation and doesn't want to be bothered with analysis.

Solution.2 : A framework that provides perspectives and better calculation.

Appendix.A : Supplementary material to Solution.1.

Appendix.B.1 : In-site links to existing questions that are closely related.

Appendix.B.2 : Supplementary material to Solution.2.

# Setup the Notations

Let $$X$$ be the total number of trials of Bob's flips when his first head (success) appears.

Let $$Y$$ be that for Bub. We have $$X\sim \mathrm{Geo}[\rho]$$ independent to $$Y\sim \mathrm{Geo}[\tau]$$.

The following basics for a Geometric distribution will be useful here: \begin{align*} \mu_{_X} &\equiv \mathbb{E}[X] = \frac1{\rho} & & \tag*{Eq.(1)} \label{Eq01} \\ A_X &\equiv \mathbb{E}[X(X+1)] = \frac2{ \rho^2 } & &\tag*{Eq.(2)} \label{Eq02} \\ S_X &\equiv \mathbb{E}[X^2 ] = \frac{2 - \rho}{ \rho^2 } & &\tag*{Eq.(3)} \label{Eq03} \\ V_X &\equiv \mathbb{V}[X] = \frac{1 - \rho}{ \rho^2 } & &\tag*{Eq.(4)} \label{Eq04} \\ Q_X &\equiv \mathbb{E}[(X+1)^2] = A_X + \mu_{_X} + 1 = \frac{ 2 + \rho + \rho^2 }{ \rho^2 } & &\tag*{Eq.(5)} \label{Eq05} \end{align*} Recall that we often use $$A_X$$ to obtain $$S_X$$ because $$A_X$$ is easier to derive (it is a more natural quantity for the Geometric distribution).

The shorthands for $$Y$$ are the same $$\mu_{_Y}$$ and $$V_Y$$ etc.

# Solution.1

Just like how the expectation can be derived (which apparently you know how to), the 2nd moment (thus variance) can be obtained by conditioning on the results of the round. Denote the events of $$\{ \text{Bob head, Bub tail} \}$$ as just $$HT$$, and recall that $$X$$ is for Bob flipping alone as if Bub doesn't exist. \begin{align*} \mathbb{E}[ R^2 ] &= \rho \tau \, \mathbb{E}\left[ R^2 \,\middle|~ HH\right] + (1 - \rho)(1 - \tau)\,\mathbb{E}\left[ R^2 \,\middle|~ TT\right] \\ &\hspace{36pt} + \rho (1 - \tau) \, \mathbb{E}\left[ R^2 \,\middle|~ HT\right] + \tau (1 - \rho)\,\mathbb{E}\left[ R^2 \,\middle|~TH\right] \\ &= \rho \tau + (1 - \rho)(1 - \tau)\,\mathbb{E} \left[ (1+R)^2 \right] + \rho (1 - \tau) \, \mathbb{E}\left[ (1+Y)^2 \right] + \tau (1 - \rho)\,\mathbb{E}\left[ (1+X)^2 \right] \end{align*} Please let me know if you need justification for $$\mathbb{E}[ R^2 ~|~~TH] = \mathbb{E}[ (1+Y)^2 ]$$ and the alike. Moving on, use the \ref{Eq04} shorthand $$Q_X$$ and $$Q_Y$$ for now and rearrange. $$\mathbb{E}[ R^2 ] = \rho \tau + (1 - \rho)(1 - \tau) \left( \mathbb{E}[ R^2 ] + 2 \mathbb{E}[ R ] + 1 \right) + \rho (1 - \tau) Q_Y + \tau (1 - \rho)\,Q_X$$ Denote $$\lambda = 1 - (1 - \rho)(1 - \tau) = \rho + \tau - \rho \tau$$, and collect $$\mathbb{E}[ R^2 ]$$ on the left. $$\begin{equation*} \lambda\,\mathbb{E}[ R^2 ] = \rho \tau + (1 - \lambda) \left( 2 \mathbb{E}[ R ] + 1 \right) + \rho (1 - \tau) Q_Y + \tau (1 - \rho)\,Q_X \tag*{Eq.(6)} \label{Eq06} \end{equation*}$$ Note that the denominator of $$\mathbb{E}[ R ]$$ is just $$\lambda$$. Along with the symmetry, this suggests that the numerator of $$\mathbb{E}[ R ]$$ can be rewritten into a better form invoking the basic \ref{Eq01}. \begin{align*} 1 + \frac{ \rho }{ \tau } + \frac{ \tau }{ \rho } - (\rho + \tau) &= \bigl( 1 + \frac{ \rho }{ \tau } - \rho \bigr) + \bigl( 1 + \frac{ \tau }{ \rho } - \tau \bigr) - 1 \\ &= \frac{ \tau + \rho - \rho\tau }{ \tau } + \frac{ \rho + \tau - \rho\tau }{ \rho } - 1 \\ \implies \mathbb{E}[ R ] = \frac1{\rho} + \frac1{\tau} &- \frac1{\lambda} = \mu_{_X} + \mu_{_Y} - \frac1{\lambda} \tag*{Eq.(7)} \label{Eq07} \end{align*} It's not a coincidence that the expectation can be expressed as such. The reason will be elaborated in the next section for Solution.2.

For the sake of computing the numeric value, \ref{Eq06} was a good place to stop. With the given parameters $$\rho = 1/3$$ and $$\tau = 2/5$$, we have $$\mathbb{E}[ R ] = 23/6$$, $$Q_X = 22$$, and $$Q_Y = 16$$. That makes $$\mathbb{E}[ R^2 ] = 190/9$$ and the variance $$V_R \equiv \mathbb{E}[ R^2 ] - \mathbb{E}[R]^2 = \frac{77}{12}~.$$ See the end of Solution.2 for a Mathematica code block for the numerical evaluation and more.

One can quickly check the value of $$\mathbb{E}[ R ] \approx 3.8333$$ relative to $$\mu_{_X} = 3$$ and $$\mu_{_Y} = 2.5$$, as well as $$V_R \approx 6.146667$$ in relation to $$V_X = 6$$ and $$V_Y = 15/4$$. Both of the quantities for $$R$$ are slightly larger than the max of $$X$$ and $$Y$$, which is reasonable.

Now, if you have a strong inclination for algebraic manipulation, then the following is what you might arrive at, after some trial and error. Recall the shorthand for the 2nd moment \ref{Eq03}: $$\begin{equation*} \mathbb{E}[ R^2 ] = \frac{ 2 - \rho }{ \rho^2 } + \frac{ 2 - \tau }{ \tau^2 } - \frac{ 2 - \lambda }{ \lambda^2 } = S_X + S_Y - \frac{ 2 - \lambda }{ \lambda^2 } \tag*{Eq.(8)} \label{Eq08} \end{equation*}$$ Again, this is not a coincidence. Along with \ref{Eq07}, their respective 3rd terms seem to be another Geometric random variable with the success' parameter $$\lambda$$. The proper probabilistic analysis is the subject of Solution.2 up next.

By the way, blindly shuffling the terms around is usually not the best thing one can do. Nonetheless, just for the record, Appendix.A shows one way of going from \ref{Eq06} to \ref{Eq08} mechanically.

# Solution.2

Denote $$W \equiv \min(X,Y)$$ as the smaller among the two and $$Z \equiv \max(X,Y)$$ as the larger. The key observation to solve this problem is that $$Z \overset{d}{=} R~, \qquad\qquad \textbf{the maximum has the same distribution as the 'rounds'.}$$ This allows one to think about the whole scenario differently (not as rounds of a two-player game). For all $$k \in \mathbb{N}$$, since $$X \perp Y$$ we have \begin{align*} \Pr\{ Z = k \} &= \Pr\{ X < Y = Z = k \} \\ &\hspace{36pt} + \Pr\{ Y < X = Z = k \} \\ &\hspace{72pt} + \Pr\{ X = Y = Z = k \} \\ &= \tau (1 - \tau)^{k-1} (1 - (1-\rho)^k) \\ &\hspace{36pt} + \rho (1 - \rho)^{k-1} (1 - (1-\tau)^k) \\ &\hspace{72pt} + \rho\tau (1 - \rho)^{k-1} (1 - \tau)^{k-1} \tag*{Eq.(9)} \label{Eq09} \end{align*} In principle, now that the distribution of $$Z$$ (thus $$R$$) is completely specified, everything one would like to know can be calculated. This is indeed a valid approach (to obtain the mean and variance), and the terms involved are all basic series with good symmetry.

Note that $$Z$$ is not Geometric (while $$X$$, $$Y$$, and $$W$$ are), nor is it Negative Binomial. At this point, it is not really of interest that \ref{Eq09} can be rearranged into a more compact and illuminating form ...... because we can do even better.

There are two more observations that allow one to not only to better calculate but also understand the whole picture. \begin{align*} &X+Y = W + Z \tag*{Eq.(10)} \label{Eq10} \\ &W \sim \mathrm{Geo[\lambda]} \tag*{Eq.(11)} \label{Eq11} \end{align*} This is the special case of the sum of the order statistics being equal to the original sum. In general with many summands this not very useful, but here with just two terms it is crucial.

Back to the two-player game scenario, the fact that $$W$$ is Geometric with success probability $$\lambda = 1 - (1 - \rho)(1 - \tau)$$ is easy to see: a round 'fails' if and only if both flips 'fail', with a probability $$(1 - \rho)(1 - \tau) = 1 - \lambda$$.

The contour of $$W = k$$ is L-shaped boundary of the '2-dim tail', which fits perfectly with the scaling nature (memoryless) of the joint of two independent Geometric distribution. Pleas picture in the figure below that $$\Pr\left\{W = k_0 + k ~ \middle|~~ W > k_0 \right\} = \Pr\{ W = k\}$$ manifests itself as the L-shape scaling away.

The contour of $$Z = k$$ looks like $$\daleth$$, and together with the L-contour of $$W$$ they make a cross. This is \ref{Eq10} the identity $$X+Y = W+Z$$, visualized in the figures below. See Appendix.B.1 for the linked in-site posts of related topics. Immediately we know the expectation to be: $$\begin{equation*} \mathbb{E}[ Z ] = \mathbb{E}[ X + Y - W ] = \mu_{_X} + \mu_{_Y} - \mu_{_W} = \frac1{\rho} + \frac1{\tau} - \frac1{\lambda} \tag*{Eq.(7.better)} \end{equation*}$$

This derivation provides perspectives different from (or better, arguably) those obtained by conditioning on the first round.

Derivation of the 2nd moment reveals a more intriguing properties of the setting. \begin{align*} \mathbb{E}[ Z^2 ] &= \mathbb{E}[ (X + Y - W)^2 ] \\ &= \mathbb{E}[ (X + Y)^2 ] + \mathbb{E}[ W^2 ] - 2\mathbb{E}[ (X + Y) W ] \\ &= \mathbb{E}[ X^2 ] + \mathbb{E}[ Y^2 ] + 2 \mathbb{E}[ XY ] + \mathbb{E}[ W^2 ] - 2\mathbb{E}[ (W+Z) W ] \qquad\because X+Y = W+Z\\ &= \mathbb{E}[ X^2 ] + \mathbb{E}[ Y^2 ] - \mathbb{E}[ W^2 ] + 2\mathbb{E}[ XY ] - 2\mathbb{E}[ WZ ] \end{align*} Here's the kicker: when $$X \neq Y$$, by definition $$W$$ and $$Z$$ each take one of them so $$WZ = XY$$, and when $$X = Y$$ we have $$WZ = XY$$ just the same!! Consequently, $$\mathbb{E}[ ZW ] = \mathbb{E}[ XY ] =\mu_{_X} \mu_{_Y}$$ always, and they cancel. $$\begin{equation*} \mathbb{E}[ Z^2 ] = \mathbb{E}[ X^2 ] + \mathbb{E}[ Y^2 ] - \mathbb{E}[ W^2 ] = \frac{ 2 - \rho }{ \rho^2 } + \frac{ 2 - \tau }{ \tau^2 } - \frac{ 2 - \lambda }{ \lambda^2 } \tag*{Eq.(8.better)} \label{Eq08better} \end{equation*}$$ This is the proper derivation, and in \ref{Eq08} the 3rd term following $$S_X + S_Y$$ is indeed $$-S_W$$.

Keep in mind that both $$W$$ and $$Z$$ are clearly correlated with $$X$$ and $$Y$$, while our intuition also says that the correlation between $$W$$ and $$Z$$ is positive (see Appendix.B for a discussion).

While we're at it, this relation in fact holds true for any (higher) moments, $$\mathbb{E}[ Z^n ] = \mathbb{E}[ X^n ] + \mathbb{E}[ Y^n ] - \mathbb{E}[ W^n ]~.$$This is true due to the same argument that gave us $$WZ = XY$$, and using \ref{Eq09} doing the explicit sums to derive this identity is also easy.

Without further ado, the variance of $$Z$$ (thus the variance of $$R$$), previously known only as the unsatisfying "\ref{Eq06} minus the square of $$\mathbb{E}[ Z ]$$'', can now be put in its proper form. \begin{align*} V_Z &\equiv \mathbb{E}[ Z^2 ] - \mathbb{E}[ Z ]^2 \\ &= \mathbb{E}[ X^2 ] + \mathbb{E}[ Y^2 ] - \mathbb{E}[ W^2 ] - ( \mu_{_X} + \mu_{_Y} - \mu_{_W} )^2 \\ &= V_X + V_Y - V_W + 2( \mu_{_W} \mu_{_Z} - \mu_{_X} \mu_{_Y}) \tag*{Eq.(12.a)} \\ &= \frac{ 1 - \rho }{ \rho^2 } + \frac{ 1 - \tau }{ \tau^2 } - \frac{ 1 - \lambda }{ \lambda^2 } + 2\left( \frac1{ \lambda } \bigl( \frac1{ \rho } + \frac1{ \tau } - \frac1{ \lambda } \bigr) - \frac1{ \rho \tau }\right) \tag*{Eq.(12.b)} \end{align*} This expression (or the symbolic one just above) is a perfectly nice formula to me. You can rearrange it to your heart's content, for example, like this $$\begin{equation*} V_Z = \bigl( \frac1{ \rho } - \frac1{ \tau } \bigr)^2 - \frac1{ \lambda^2 } + 2 (\frac1{\lambda} - \frac12) \bigl( \frac1{ \rho } + \frac1{ \tau } - \frac1{ \lambda } \bigr) \tag*{Eq.(12.c)} \end{equation*}$$ which emphasizes the role played by the difference between $$X-Y$$.

I'm not motivated to pursue a 'better' form beyond Eq.(12), and if anyone knows any alternative expressions that are significant either algebraically or probabilistically, please do share.

Let me conclude with a Mathematica code block verifying the numeric values and identities for the variance, the 2nd moment, as well as the earliest algebra of the conditioning of expectation.

(* t = \tau, p = \rho, m = E[R],  S = E[R^2] , h = \lambda = 1-(1-p)(1-t) *) ClearAll[t , p, m, S, V, h, tmp, simp, sq, var, basics]; basics[p_] := {1/p, (1 - p)/p^2, (2 - p)/p^2, (2 + p + p^2)/ p^2};(* EX, VX, E[X^2], E[(1+X)^2]*)
Row@{basics[1/3], Spacer@10, basics[2/5], Spacer@10, basics[1 - (1 - 1/3) (1 - 2/5)]}
simp = Simplify[#, 0 < p < 1 && 0 < t < 1] &; h = 1 - (1 - p) (1 - t); m = (1 + t/p + p/t - (t + p))/h; sq[a_] := (2 - a)/a^2;(* 2nd moment of a Geometric distribution *) var[a_] := (1 - a)/a^2;
S = tmp /. Part[#, 1]&@Solve[tmp == p t + (1 - p) (1 - t) (1 + 2 m + tmp) + p (1 - t) (1 + 2/t + sq@t) + (1 - p) t (1 + 2/p + sq@p), tmp] //simp (* this simplification doesn't matter ... *)
V = S - m^2 // simp (* neither does this. *)
{tmp = V /. {p -> 1/3, t -> 2/5}, tmp // N} (* below: veryfiy the various identities for 2nd moment and variance. Difference being zero means equal *)
{sq@t + sq@p - sq@h - S, var@p + var@t - var@h + 2 m 1/h - 2/(p t) - V, (1/p - 1/t)^2 - 1/h^2 + 2 (1/p + 1/t - 1/h) (1/h - 1/2) - V} // simp
`

$$\large\textbf{Appendix.A: }\normalsize\text{an algebraic route from \ref{Eq06} to \ref{Eq08}}$$

Consider \ref{Eq06} one part at a time. First, the $$2(1 - \lambda)\, \mathbb{E}[ R ]$$. From the the first line to the 2nd line, $$1 - \lambda = (1 - \rho)(1 - \tau)$$ is inserted for the 2nd term: \begin{align*} 2(1 - \lambda)\, \mathbb{E}[ R ] &= 2(1 - \lambda )\frac{ - 1}{\lambda} + 2(1 - \lambda) \bigl( \frac1{ \rho } + \frac1{ \tau } \bigr) \\ &= 2\frac{\lambda - 1}{\lambda} + \frac{2(1 - \rho ) }{ \rho }(1 - \tau) + (1 - \rho ) \frac{ 2(1 - \tau) }{ \rho } \\ &= 2 - \frac2{\lambda} + \bigl( \frac{2 - \rho}{ \rho } - 1\bigr) (1 - \tau) + \bigl( \frac{2 - \tau}{ \tau } - 1 \bigr) (1 - \rho ) \\ &= \color{magenta}{2 - \frac2{\lambda} } + \frac{2 - \rho}{ \rho^2 } (1 - \tau)\rho \color{magenta}{- (1 - \tau)} + \frac{2 - \tau}{ \tau^2 } (1 - \rho )\tau \color{magenta}{- (1 - \rho )} \end{align*} Next in line are the $$Q_X$$ terms. \begin{align*} \rho (1 - \tau) Q_Y + \tau (1 - \rho)\,Q_X &= \rho (1 - \tau) \bigl( \frac{ 2 + \tau }{ \tau^2 } + 1\bigr) + \tau (1 - \rho) \bigl( \frac{ 2 + \rho }{ \rho^2 } + 1 \bigr) \\ &= \rho \bigl( \frac{ 2 - \tau - \tau^2 }{ \tau^2 } + 1 - \tau \bigr) + \tau \bigl( \frac{ 2 - \rho - \rho^2 }{ \rho^2 } + 1 - \rho \bigr) \\ &= \rho \frac{ 2 - \tau}{ \tau^2 } + \tau \frac{ 2 - \rho }{ \rho^2 } \color{magenta}{- 2\tau\rho} \end{align*} Put things back together in \ref{Eq06}, the magenta terms combine with $$\rho\tau$$ and $$1 - \lambda$$ (multiplied by 1): \begin{align*} \lambda\,\mathbb{E}[ R^2 ] &= \rho \tau + (1 - \lambda) + (1 - \lambda) 2 \mathbb{E}[ R ] + \rho (1 - \tau) Q_Y + \tau (1 - \rho)\,Q_X \\ &= \rho \tau + (1 - \lambda) \color{magenta}{ {}+ 2 - \frac2{\lambda} - (1 - \tau) - (1 - \rho ) - 2\tau\rho} \\ &\hphantom{{}= \rho \tau} + \frac{2 - \rho}{ \rho^2 } (1 - \tau)\rho + \frac{2 - \tau}{ \tau^2 } (1 - \rho )\tau \tag{from \mathbb{E}[R]}\\ &\hphantom{{}= \rho \tau } + \tau \frac{ 2 - \rho }{ \rho^2 } + \rho \frac{ 2 - \tau}{ \tau^2 } \tag{from Q_X etc}\\ &= 1 - \frac2{\lambda} + \frac{ 2 - \rho }{ \rho^2 } \bigl( \rho + \tau - \rho\tau \bigr) + \frac{ 2 - \tau }{ \tau^2 } \bigl( \rho + \tau - \rho\tau \bigr) \\ &= 1 - \frac2{\lambda} + \frac{ 2 - \rho }{ \rho^2 } \lambda + \frac{ 2 - \tau }{ \tau^2 } \lambda \end{align*} Finally we can divide by the coefficient of $$\mathbb{E}[ R^2 ]$$ on the left hand side and obtain \ref{Eq08}.

$$\large\textbf{Appendix.B.1: }\normalsize\text{In-Site Links to Related Questions}$$

I found many existing posts about this topic (minimum of two independent non-identical Geometric distributions). In chronological order: 90782, 845706, 1040620, 1056296, 1169142, 1207241, and 2669667.

Unlike the min, only 2 posts about max is found so far: 971214, and 1983481. Neither of the posts go beyond the 1st moment, and only a couple of the answers address the 'real geometry' of the situation.

In particular, consider the trinomial random variable $$T$$ that splits the 2-dim plane into the 3 regions. $$\begin{equation*} T \equiv \mathrm{Sign}(Y - X) = \begin{cases} \hphantom{-{}} 1 & \text{if}~~Y > X \quad \text{, with probability}~~ \tau( 1 - \rho) / \lambda \\ \hphantom{-{}} 0 & \text{if}~~X = Y \quad \text{, with probability}~~ \rho \tau / \lambda \\ -1 & \text{if}~~Y < X \quad \text{, with probability}~~ \rho( 1 - \tau) / \lambda \end{cases} \end{equation*}$$ This trisection of above-diagonal, diagonal, and below-diagonal is fundamental to the calculation of both $$W$$ and $$Z$$.

It can be easily shown that $$T \perp W$$, that the trisection is independent of the minimum. For example, $$\Pr\left\{ T = 1 \mid W = k\right\} = \Pr\{ T = 1\}$$ for all $$k$$.

Note that $$T$$ is not independent to $$Z$$ (even with the corner $$k=1$$ removed).

In the continuous analogue, $$\{X,Y,W\}$$ are Exponential with all the nice properties, and $$Z$$ is neither Exponential nor Gamma (analogue of Negative Binomial).

The density of the $$Z$$ analogue is easy and commonly used, but its doesn't seem to have a name. At best one can categorize it as a special case of phase-type (PH distribution).

$$\large\textbf{Appendix.B.2: }\normalsize\text{Covariance between \min(X,Y) and \max(X,Y), along with related discussions.}$$

Recall the expectations: $$\mathbb{E}[X] = \mu_{_X} = 1 / \rho$$, and the similar \begin{align*} \mu_{_Y} &= \frac1{ \tau }~, & \mu_{_W} &= \frac1{ \lambda } = \frac1{\rho + \tau - \rho\tau}~, & &\text{and}\quad \mu_{_Z} = \mu_{_X} + \mu_{_Y} - \mu_{_W} \end{align*} The covariance between the minimum and the maximum is $$\begin{equation*} C_{WZ} \equiv \mathbb{E}\left[ (W - \mu_{_W} ) (Z - \mu_{_Z} ) \right] = \mathbb{E}[ WZ ] - \mu_{_W} \mu_{_Z} = \mu_{_X} \mu_{_Y} - \mu_{_W} \mu_{_Z} \tag*{Eq.(B)} \label{EqB} \end{equation*}$$ The last equal sign invokes the intriguing $$WZ = XY$$ mentioned just before \ref{Eq08better}.

A positive covariance (thus correlation) expresses the intuitive idea that when the min is large then the max also tends to be large.

Here we do a 'verbal proof' of $$C_{WZ} > 0$$ for all combinations of parameters. The purpose is not really a technical proof but more about illustrating the relations between $$\{W, Z\}$$ and $$\{X, Y\}$$.

Since $$\mu_{_X} + \mu_{_Y} = \mu_{_W} + \mu_{_Z}$$, the covariance $$C_{WZ}$$ is in essence comparing products (of two non-negative numbers) given the value of the sum. We know that for a fixed sum, the closer the two 'sides' (think rectangle) are the larger the product.

Being the min and max, $$\mu_{_W}$$ and $$\mu_{_Z}$$ are more 'extreme' and can never be as close as $$\mu_{_X}$$ and $$\mu_{_Y}$$, throughout the entire parametric space $$\{ \rho, \tau \} \in (0, 1)^2$$. Boom, QED.

(the extreme cases where at least one of $$\{\rho, \tau\}$$ is zero or one are all ill-defined when it comes to min and max)

An algebraic proof for $$C_{WZ} > 0$$ (or for everything in this appendix) is easy. Let me emphasize the descriptive aspect.

Consider what it means for $$W$$ as a Geometric distribution to be the minimum of $$X$$ and $$Y$$. \begin{align*} \frac1{\lambda} &< \min( \frac1{\rho}, \frac1{\tau} ) & &\text{faster to 'succeed' on average} \\ \lambda &> \max( \rho, \tau ) & &\text{better than the higher success parameter} \end{align*} That is, the mean of the 'minimum flip' $$\mu_{_W} < \min( \mu_{_X}, \mu_{_Y} )$$ is more extreme at the lower end.

On the other hand, $$Z$$ is NOT a Geometric distribution. However, one can define an auxiliary $$Z' \sim \mathrm{Geo}[1 / \mu_{_Z}]$$ with an equivalent mean $$\mu_{_{Z'}} = \mu_{_Z}$$. Once we have arrived at \ref{EqB}, the role of $$Z$$ can be replaced by $$Z'$$: $$C_{WZ} = C_{WZ'} = \mu_{_X} \mu_{_Y} - \mu_{_W} \mu_{_{Z'}}$$

Now we can have similar descriptions for $$\mu_{_Z}$$, while it is actually $$\mu_{_{Z'}}$$ for the Geometric distribution that is being compared with $$X$$ and $$Y$$. \begin{align*} \mu_{_Z} &> \max( \mu_{_X}, \mu_{_Y} ) & &\text{slower to 'succeed' on average} \\ \frac1{ \mu_{_Z} } &< \min( \rho, \tau ) & &\text{worse than the lower success parameter} \end{align*} That is, the mean of the 'maximum flip' is more extreme at the higher end.

This concludes the verbal argument for how $$\mu_{_W}$$ and $$\mu_{_Z}$$ are more dissimilar (than the relation between $$\mu_{_X}$$ and $$\mu_{_Y}$$) thus making a smaller product.

• Thank you for this comprehensive, inspiring answer. Solution One I don't have problem understanding, I'm upset a bit that I didn't complete my analysis. I turned back while trying to form this recursion involving nonlinear terms, like E[(1+X)^2]. Solution Two and the Appendices I am able to follow as well.
– BoLe
Mar 22, 2018 at 10:45