A Monte Carlo EM method of nonstationary covariance model and estimator proposed in the main text is illustrated with an application to surface ozone data from an irregularly distributed environmental monitoring network. It is useful to display the predicted covariances visually to convey the form of nonstationarity. Figure 1 shows nonstationary covariance structures of the surface ozone, obtai...

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions exc...

In mixed effects model, observations are a function of fixed and random effects and an error term. This structure determines a very specific structure for the variances and covariances of these observations. Unfortunately, the specific parameters of this variance/covariance structure might not be identifiable. Nonidentifiability can lead to complications in numerical estimation algorithms or wo...

[1] Minimum Variance Analysis (MVA) is frequently used for the geometrical organization of a time series of vectors. The Coplanarity Variance Analysis (CVA) developed in this paper reproduces the layer geometry involving coplanar magnetosonic shocks or plane-polarized wave trains (including normals and coplanarity directions) 300 times more precisely (<0.1 ) than MVA using the same input data. ...

A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive × additive, do...

This paper provides asymptotic bias and variance analysis for MIMO system estimates obtained by using generalizations of FIR model structures and least squares techniques. The generalizations are such that prior approximate knowledge of the system poles may be incorporated. The obtained variance expressions provide extensions to well known results that have previously been derived only for FIR ...

Using high resolution magnetic resonance imaging data, we examined the interrelationships between eight cerebral lobar volumetric measures via both exploratory and confirmatory factor analyses in a large sample (N = 484) of pediatric twins and singletons. These analyses suggest the presence of strong genetic correlations between cerebral structures, particularly between regions of like tissue t...

Longitudinal data are important in exposure and risk assessments, especially for pollutants with long half-lives in the human body and where chronic exposures to current levels in the environment raise concerns for human health effects. It is usually difficult and expensive to obtain large longitudinal data sets for human exposure studies. This article reports a new simulation method to generat...