# Probably Approximately Wrong

## An infrequent blog, by Nicola Branchini

I came across this 1.25 page paper by Don Rubin and Sanford Weisberg (Rubin & Weisberg) in Biometrika from 1975. Good old times.

It considers the problem of finding the “best” linear combination (whose weights sum to 1 !) of $K$ estimators of the same quantity. The estimators are all assumed to be unbiased, and independent. I think this is still a very much relevant topic; however, I won’t try to convince you of this, because I want to keep this short. If anything, it can be seen as a fun little exercise. The result is simple, so probably has been used independently by many authors, without them being aware of this paper (which only has 18 citations!).

We let $\tau$ be the true, unknown quantity of interest. Estimators of $t$ will just be sub-indexed, as $t_1,\dots,t_K$. These are mutually independent (not necessarily i.i.d.) and unbiased. We will assess the quality of the estimators by their mean squared error. We now define an estimator: $\widehat{t} := \sum_{k=1}^{K} \hat{\alpha}_{k} t_{k}$, with the weights $\hat{\alpha}_k$ be random variables and such that $\sum_{k=1}^{K} \widehat{\alpha_{k}} = 1$. They are independent of $t_1,\dots,t_K$. We will see that the $\hat{\alpha}_k$’s need not be mutually independent in order for the result to hold. That’s all the assumptions on the distribution of the weights. We further denote the variance of the individual estimators $t_{k}$ as $V_{k}$.

Why did they define weights as random variables ? As we shall see, because the optimum weights involve a quantity that needs to be estimated. That is, $\widehat{t}$ is the estimator we can actually use, and we will compare it to some intractable optimum solution.
We see that $\widehat{t}$ is unbiased by applying the law of iterated expectation (and the law of the unconsciuous statistician):

\begin{equation}\begin{aligned} \mathbb{E}[\widehat{t}] = \mathbb{E}_{\mathbf{P}_{\widehat{t}}}[\widehat{t}] = \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}}[\mathbb{E}_{\bigotimes_k \mathbf{P}_{t_{k} | \widehat{\boldsymbol{\alpha}}}}[ \widehat{t} | \widehat{\alpha}_{1}, \dots, \widehat{\alpha}_{K}]] = \tau \cdot \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \left (\sum_{k=1}^{K} \widehat{\alpha_{k}} \right ) \right ] = \tau \end{aligned}\end{equation}\tag{1}\label{eq1}

where $\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}$ is the joint law of the $\widehat{\boldsymbol{\alpha}} := [\widehat{\alpha}_{1},\dots,\widehat{\alpha}_{K}]^\top$, $\bigotimes_k \mathbf{P}_{t_{k} | \widehat{\boldsymbol{\alpha}}}$ the conditional of $\widehat{t}$ given $\widehat{\boldsymbol{\alpha}}$, and $\mathbf{P}_{\widehat{t}}$ the marginal of $\widehat{t}$. If you are not familiar with $\bigotimes_k \mathbf{P}$, it just means a joint which factorizes as the product of its $K$ marginals. Note that actually we need the weights to sum to $1$ only in expectation, for unbiasedness. However, we will need that they sum to 1 for all realizations of the random variables for the next derivation.1

Because of the unbiasedness, the mean squared error of the estimator $\widehat{t}$ will be just equal to its variance, for which we apply the law of total variance: \begin{equation}\begin{aligned} \mathbb{V}[\widehat{t}] = \mathbb{V}_{\mathbf{P}_{\widehat{t}}}[\widehat{t}] &= \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \mathbb{V}_{\bigotimes_k \mathbf{P}_{t_{k} | \widehat{\boldsymbol{\alpha}}}} \left [ \widehat{t} | \widehat{\alpha}_{1}, \dots, \widehat{\alpha}_{K} \right ] \right ] + \mathbb{V}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \mathbb{E}_{\bigotimes_k \mathbf{P}_{t_{k} | \widehat{\boldsymbol{\alpha}}}} \left [ \widehat{t} | \widehat{\alpha}_{1}, \dots, \widehat{\alpha}_{K} \right ] \right ] \\ &= \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \mathbb{V}_{\bigotimes_k \mathbf{P}_{t_{k} | \widehat{\boldsymbol{\alpha}}}} \left [ \sum_{k=1}^{K} \hat{\alpha}_{k} t_{k} \right ] \right ] + \tau^2 \cdot \underbrace{\mathbb{V}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \sum_{k=1}^{K} \widehat{\alpha_{k}} \right ]}_{=~ 0} \\ &= \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \sum_{k=1}^{K} \hat{\alpha}_{k}^{2} V_{k} \right ] . \end{aligned}\end{equation}\tag{2}\label{eq2} In the third line, the second term is $0$ since the variance of a constant (here, $1$) is $0$. At this point Rubin & Weisberg use a little trick to link this variance to that of the optimum one. That is, the best variance possible in this setting. Let us define $t^\star := \sum_{k=1}^{K} \alpha_{k}^{\star} t_{k}$ (the paper uses $\alpha_{k}$ instead, but my notation is better). The optimum weights are now deterministic, and they can be shown to be equal to $\alpha_{k}^{\star} = \frac{1}{W \cdot V_{k}}$ with $W = \sum_{k=1}^{K} \frac{1}{V_k}$. Therefore, let the optimum estimator be: \begin{equation}\begin{aligned} t^\star := \sum_{k=1}^{K} \alpha_{k}^{\star} t_{k} = \frac{\sum_{k=1}^{K} \frac{1}{V_{k}} t_{k}}{\sum_{k^\prime=1}^{K} \frac{1}{V_{k^\prime}}} . \end{aligned}\end{equation}\tag{3}\label{eq3}

Note that by design $\sum_{k=1}^{K} \alpha_{k}^{\star} = 1$. Now that weights are deterministic, it is even more obvious that $\mathbb{E}[t^\star] = \tau$. The variance of $t^\star$ is readily seen as $\mathbb{V}[t^\star] = \mathbb{V}_{\mathbf{P}_{t^\star}}[t^\star]= \mathbb{V}_{\bigotimes_k \mathbf{P}_{t_{k}}}[\sum_{k=1}^{K} \alpha_{k}^{\star} t_{k}] = \sum_{k=1}^{K} (\alpha_{k}^{\star})^2 V_{k}$. Now we are going to express the variance of $t^\star$ in a way that will allows us for comparison witht that of $\widehat{t}$ (and indeed, prove $\mathbb{V}[t^\star] \leq \mathbb{V}[\widehat{t}]$). We write: \begin{equation}\begin{aligned} \require{cancel} \mathbb{V}[t^\star] = \sum_{k=1}^{K} (\alpha_{k}^{\star})^2 V_{k} = \frac{\sum_{k=1}^{K} \left ( \frac{1}{V_{k}} \right )^2 \cdot V_{k}}{W^2} = \frac{\cancel{\sum_{k=1}^{K} \frac{1}{V_{k}}}}{\cancel{W} \cdot W} = \frac{1}{W} = \frac{1}{V_{k} W} \cdot V_{k} = \alpha_{k} V_{k} . \end{aligned}\end{equation}\tag{4}\label{eq4} Now, the trick is to add and subtract $\mathbb{V}[t^\star]$ from \eqref{eq2}, and replacing $V_{k}$’s for $\frac{\mathbb{V}[t^\star]}{\alpha_{k}^{\star}}$ (given to us by \eqref{eq4}): \begin{equation}\begin{aligned} \mathbb{V}[\widehat{t}] &= \mathbb{V}[t^\star] + \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \sum_{k=1}^{K} \hat{\alpha}_{k}^{2} \color{LimeGreen}{V_{k}} \right ] - \overbrace{\sum_{k=1}^{K} (\alpha_{k}^{\star})^2 \color{LimeGreen}{V_{k}}}^{=~\mathbb{V}[t^\star]} \\ &= \mathbb{V}[t^\star] + \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \sum_{k=1}^{K} \hat{\alpha}_{k}^{2} \color{LimeGreen}{\frac{\mathbb{V}[t^\star]}{\alpha_{k}^{\star}}} \right ] - \sum_{k=1}^{K} (\alpha_{k}^{\star})^{2} \color{LimeGreen}{\frac{\mathbb{V}[t^\star]}{\alpha_{k}^{\star}}} \\ &= \mathbb{V}[t^\star] \left [ 1 + \sum_{k=1}^{K} \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \frac{\left ( \widehat{\alpha_{k}} -(\alpha_{k}^{\star})^2 \right )^2}{\alpha_{k}^{\star}} \right ] \right ] \\ &= \mathbb{V}[t^\star] \left [ 1 + \sum_{k=1}^{K} \alpha_{k}^{\star} \mathbb{E}_{\mathbf{P}_{\widehat{\boldsymbol{\alpha}}}} \left [ \left (\frac{ \widehat{\alpha_{k}} -(\alpha_{k}^{\star})^2 }{\alpha_{k}^{\star}} \right )^2 \right ] \right ] . \end{aligned}\end{equation}\tag{5}\label{eq5} Now we see that, indeed, since the rightmost term is always positive, $\mathbb{V}[\widehat{t}] \geq \mathbb{V}[t^\star]$. The authors note that $\mathbb{V}[\widehat{t}]$ depends on $\widehat{\boldsymbol{\alpha}}$ (which we can think of as estimates for the $\alpha_{k}^{\star}$’s) only through their squared error to $\alpha_{k}^{\star}$. Therefore, it does not matter whether the estimators $\widehat{\alpha}_{1},\dots,\widehat{\alpha}_{K}$ are dependent or not.

## Thoughts

A little food for thought (on which I won’t elaborate too much, since well, this is a blogpost).

• From the last line of \eqref{eq5}, we see that it doesn’t matter whether the weight estimates are positive or negative.
• We also see that correlation between the weight estimates $\widehat{\alpha}_{K}$ does not influence the variance
• What is the most restrictive constraint here? The unbiasedness? The independence of the $t_k$’s ?

### Footnotes

1: I could have written \eqref{eq1} as just $\mathbb{E}[\widehat{t}] = \mathbb{E}_{\alpha_{k}}[\mathbb{E}[\widehat{t} | \alpha_{1}, \dots, \alpha_{K}]] = \tau$. Wouldn’t that be nicer. Compact notation is great if you know exactly what the writer’s doing - I want to force the reader to recall all the assumptions that are being made.

Rubin, D.B. and Weisberg, S., 1975. The variance of a linear combination of independent estimators using estimated weights. Biometrika, 62(3), pp.708-709.

Cited as:

@article{branchini2022combining,
title   = Combining independent and unbiased estimators,
author  = Branchini, Nicola,
journal = https://www.branchini.fun,
year    = 2022,
}


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