# Estimation of measurement reliability

I have the following statistical setting:

I have two unknowns $$a$$ and $$b$$ an I have two measurements of their values corrupted by noise distibuted uniformly in $$[-e,e]$$ and I have exact value of $$e$$ known to me beforehand. I also have a measurement of $$d = a - b$$ obtained by another method. This measurement is corrupted by a Gaussian noise with variance that I don't know in general, but I can obtain it's estimate in principle if it's necessary. Also this estimate $$d$$ can be subject to a gross error (outlier) with very large variance so in general the error in $$d$$ is given by a mixture of a Gaussian and a uniform. The mixing parameter that determines relative weight of a Gaussian and a uniform in the mixture is not known to me and cannot be obtained by any means. Also the method for measuring difference of $$a$$ and $$b$$ is way more accurate then separate estimates of $$a$$ and $$b$$ when there is no outlier in the mix.

My goal is to establish a reliability measure of measurement $$d=a-b$$. It's clear that individual measurements of $$a$$ and $$b$$ can in certain cases help me identify outliers in $$d$$. If we consider all possible values of a and b that could have resulted in the measurements that I observe, there may be no possible pair that is close to obtained measurement of $$d$$ and it clearly suggests that there's an outlier in such a case.

This reliability measure is supposed to be used in a big least squares estimator. I have millions of $$a$$'s and $$b$$'s and millions of measurements of their differences. I do a big least squares fit of all this data by minimizing a weighted sum of residuals given by individual measurements and pairwise difference measurements and I want to weight pairwise differences by some weight that reflects their reliability and it's really important to come up with such a measure since outliers will affect least squares alot if they are not attended to.

I see many heuristical ways to cook up such a measure, the most simple being 0 weight to difference measurements that completely disagre with observed individual measurements and 1 otherwise.

But maybe there is a better way of doing this. Do you guys see some non heuristical theoretically sound approach to divise a reliability measure that is to be used in least squares (essentially Maximum Likelihood) estimator in such a setting?

Your measurement process is somewhat complex. The following is the case

$$\begin{split} a &= {\cal A} + \epsilon \\ b &= {\cal B} + \epsilon \\ \epsilon &\sim {\rm u}(-e,e) \\ \\ d &= ({\cal A} - {\cal B}) + \xi \\ \xi &\sim {\rm g}(0,\sigma^2) \end{split}$$

where u denotes the uniform distribution and g the normal distribution.

Hence, your noise process looks as follows

$$d_i = \left ( {\cal A} - {\cal B} \right) \circledast g(0,\sigma^2)$$

with $$\circledast$$ the convolution operator and $$i$$ the index of the i'th measurement in your sample.

Robustness

You require a reliability measure for the measurement $$d$$. For a classification problem where $$d$$ has to exceed some threshold $$\tau$$ to be assigned class label $$1$$, there is a solution at hand. One such attempt is called robustness, which is defined as follows

"The robustness of a classification of a case is the probability that the case would obtain the same class label if the (unknown) true attribute values were known." [Egmont-Petersen, 1997]

In the reference, the robustness measure $$\varrho$$ is defined as

$$\begin{split} \varrho(d) = \int_{S} p({\cal D} \mid d) \, d{\cal D} \end{split}$$

where $${\cal D}$$ is the true but unknown value whereas $$d$$ is the observed value with all the noise in it. The region $$S$$ indicates the part of the measurement space (the interval) that is associated with the measured outcome $$d$$. The decision threshold $$\tau$$ demarcates the boundary of the interval $$S$$.

So the measure $$\varrho$$ integrates the conditional density $$p({\cal D} \mid d)$$, over the interval where $${\cal D}$$ would have resulted in the same class outcome $$1$$.

The density $$p({\cal D} \mid d)$$ is unknown, but we know $$p(d \mid {\cal D})$$ from the convolution formula above. This convolution does have an analytical solution.

Now to robustness, we apply Bayes rule

$$\begin{split} \varrho(d) = \int_{S} \frac{p(d \mid {\cal D}) p({\cal D})}{p({\cal d})} \, d{\cal D} \end{split}$$

and you have a reliability measure for your measurement $$d$$ - the robustness $$\varrho(d)$$. In practical sense, $$p(d \mid {\cal D})$$ is given by the convolution above. The density $$p({\cal D})$$ needs to be obtained from some controlled experiment with repeated measurements. Finally, $$p({\cal d})$$ is just the distribution of $$d$$ over all your data. This picture shows the computation of $$\varrho(d)$$ for a given measurement $$d$$, for a two-class situation.

Reference

M. Egmont-Petersen, J.L. Talmon, A. Hasman1, "Robustness metrics for measuring the influence of additive noise on the performance of statistical classifiers," International Journal of Medical Informatics, Vol. 46, No. 2, pp. 103-112, 1997.

• Thank you very much for comment! I will look into the paper, from your comment alone I do not completely get it. However there is one thing in your post that is not correct - when I measure d there is no uniform noise in this measurement, it's only in individual measurements. Does what you suggest still hold given this fact? Jul 30, 2020 at 22:29