An approximate multivariate Student-t likelihood is derived for the convolution of an inverse-Wishart-based Student-t with Gaussian errors by matching covariance and multivariate kurtosis.
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Methodological choices in dark siren cross-correlations can mitigate biases in H0 inference when selection effects are built into the model and samples of precise events are sufficiently large.
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On combining estimated and analytic covariance matrices
An approximate multivariate Student-t likelihood is derived for the convolution of an inverse-Wishart-based Student-t with Gaussian errors by matching covariance and multivariate kurtosis.
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Dark siren cross-correlations and the sensitivity of $H_0$ to methodological choices
Methodological choices in dark siren cross-correlations can mitigate biases in H0 inference when selection effects are built into the model and samples of precise events are sufficiently large.