This article reviews developments in statistics for spatial point processes obtained within
roughly the past decade. These developments include new classes of spatial point process …

Various denoising methods have been proposed to clean up the noise in Monte Carlo (MC)
renderings, each having different advantages, disadvantages, and applicable scenarios. In …

Data from both a randomized trial and an observational study are sometimes simultaneously
available for evaluating the effect of an intervention. The randomized data typically allows for …

We consider a robust version of the classical Wald test statistics for testing simple and
composite null hypotheses for general parametric models. These test statistics are based on …

The ability to obtain reliable point estimates of model parameters is of crucial importance in
many fields of physics. This is often a difficult task given that the observed data can have a …

Given a set of image denoisers, each having a different denoising capability, is there a
provably optimal way of combining these denoisers to produce an overall better result? An …

Many signal processing applications require performing statistical inference on large
datasets, where computational and/or memory restrictions become an issue. In this big data …

Many statistical methods (estimators) are available for estimating the consensus value (or
average effect) and heterogeneity variance in interlaboratory studies or meta‐analyses …

In this work, a novel subspace-based method for blind identification of multichannel finite
impulse response systems is presented. Here, we exploit directly the block Toeplitz …

Graph smoothing methods are an extremely popular family of approaches for semi-
supervised learning. The choice of graph used to represent relationships in these learning …