Distribution of the maximum of correlated receiver horizons

ID: distribution-of-the-maximum-of-correlated-receiver-horizons

The distribution of the maximum of correlated receiver horizons must use the joint timing law. Let . Then , which generally is not the product of the marginal probabilities. The independent special case is derived in channel switching under uncertainty in a combined receiver horizon.
For an explicit correlated model, suppose has a bivariate normal distribution with common mean , common standard deviation , and correlation coefficient . With , the cumulative distribution function and density of the maximum are
Here are the standard normal distribution density and cdf, and is the bivariate cdf. The density follows by differentiating along both boundaries and using the conditional normal law. The official SciPy reference supplies a cdf method parameterized by the mean vector and covariance matrix; it is a numerical evaluation, not an assumption of channel independence.[ref]
The equal-variance model also gives exact moments. Write , with and . They are independent normal variables, with . The absolute-normal moment then gives
For an original shared-clock example, let , , and . Then and the standard deviation is about . Positive correlation reduces the expected gain from selecting the larger horizon, while increasing its variance relative to the independent equal-channel case.
A common-clock contribution to a maximum receiver horizon can be modeled for nonnegative by with independent standard normal inputs. As tends to one, both channels share the same uncertain horizon: selection does not remove the common error. Actual receiver timing correlations must be justified by calibration and reference reuse; the example neither measures them nor guarantees that the receiver is invertible.

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