Subscribe: Biometrika - Advance Access
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Biometrika Advance Access

Published: Fri, 15 Sep 2017 00:00:00 GMT

Last Build Date: Fri, 15 Sep 2017 05:47:23 GMT


Estimating network edge probabilities by neighbourhood smoothing


The estimation of probabilities of network edges from the observed adjacency matrix has important applications to the prediction of missing links and to network denoising. It is usually addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but this is ill-defined without strong assumptions on the network structure. Here we propose a novel computationally efficient method, based on neighbourhood smoothing, to estimate the expectation of the adjacency matrix directly, without making the structural assumptions that graphon estimation requires. The neighbourhood smoothing method requires little tuning, has a competitive mean squared error rate and outperforms many benchmark methods for link prediction in simulated and real networks.

Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies


Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.

Contours and dimple for the Gneiting class of space-time correlation functions


We offer a dual view of the dimple problem related to space-time correlation functions in terms of their contours. We find that the dimple property (Kent et al., 2011) in the Gneiting class of correlations is in one-to-one correspondence with nonmonotonicity of the parametric curve describing the associated contour lines. Further, we show that given such a nonmonotonic parametric curve associated with a given level set, all the other parametric curves at smaller levels inherit the nonmonotonicity. We propose a modified Gneiting class of correlations having monotonically decreasing parametric curves and no dimple along the temporal axis.