We introduce a broad class of semiparametric estimates of the long memory parameter for stationary time series. A leading \Box-Cox" sub-class, indexed by a single tuning parameter, interpolates between the popular log periodogram and local Whittle estimates, leading to a smooth interpolation of asymptotic variances. The bias of these two estimates also di ers to higher order, and we also show h...

The uniaxial compressive strength (UCS) of intact rocks is an important geotechnical parameter required for designing geotechnical and mining engineering projects. Obtaining accurate estimates of the rock mass UCS parameter throughout a 3D geological model of the deposit is vital for determining optimum rock slope stability, designing new exploratory and blast boreholes, mine planning, optimizi...

The authors explore likelihood-based methods for making inferences about the components of variance in a general normal mixed linear model. In particular, they use local asymptotic approximations to construct confidence intervals for the components of variance when the components are close to the boundary of the parameter space. In the process, they explore the question of how to profile the re...

The Grubbs’ measurement model is frequently used to comparing several measuring devices. It is common to assume that the random terms have a normal distribution. However, such assumption makes the inference vulnerable to outlying observations whereas scale mixtures of normal distributions have been an interesting alternative to produce robust estimates keeping the elegancy and simplicity of the...

Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization. The algorithm relies on the use of fully factorized variational distributions. However, this “mean-field” independence approximation limits the fidelity of the posterior approximation, and introduces local optima. We show how to relax the mea...

A new approach to finding good local maxima of the likelihood function based on synthesizing information from two local maxima is presented. We investigate the coupled EM algorithm (CoEM) for coupling local maxima solutions from two separate EM runs for the multinomial mixture model. The CoEM algorithm probabilistically splits and merges multiple latent states based on conditional independence ...

The aim of this paper is to derive local influence curvatures under various perturbation schemes for elliptical linear models with longitudinal structure. The elliptical class provides a useful generalization of the normal model since it covers both lightand heavy-tailed distributions for the errors, such as Student-t, power exponential, contaminated normal, among others. It is well known that ...

When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with many local minima separated by high energy barriers. The commonly used Gibbs sampler tends to get trapped in one local mode, which often results in unstable learning dynamics and leads to poor parameter estimates. In this...