The <Emphasis FontCategory="NonProportional">RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techniques are presented for effectively using <Emphasis FontCategory="NonProportional">RngStreams with <Emphasis FontCategory="NonProportional">C++ programs that are parallelized via <Emphasis FontCategory="NonProportional">OpenMP or <Emphasis FontCategory="NonProportional">MPI. Ways...</emphasis></emphasis></emphasis></emphasis></emphasis>

Many applications in data analysis rely on the decomposition of a data matrix into a low-rank and a sparse component. Existing methods that tackle this task use the nuclear norm and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\ell _1$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>ℓ</mi> <mn>1</mn> </msub> </math> </EquationSource> </InlineEquation>-cost functions as convex relaxations of the rank constraint and the sparsity measure,...</equationsource></equationsource></inlineequation>

The Clayton models, also called gamma frailty models, have been widely used for multivariate survival analysis. These models typically appear in either conditional or marginal formulations where covariates are incorporated through regression models. The two formulations provide us the...

Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. A procedure to select balanced samples in a stratified population has previously been proposed. Unfortunately, this procedure becomes very...

Interval-censored survival data are often recorded in medical practice. Although some methods have been developed for analyzing such data, issues still remain in terms of efficiency and accuracy in estimation. In addition, interval-censored data with spatial correlation are not unusual but less...

Gaussianity tests have being widely studied in the literature. Regarding the study of Gaussianity tests for stationary processes, these only verify the Gaussianity of a marginal at a fixed finite order, generally order one. Therefore, they do not reject stationary non-Gaussian processes with the...

It is well known that in the context of the classical regression model with heteroskedastic errors, while ordinary least squares (OLS) is not efficient, the weighted least squares (WLS) and quasi-maximum likelihood (QML) estimators that utilize the information contained in the heteroskedasticity...

The problem of estimating a nonlinear state-space model whose state process is driven by an ordinary differential equation (ODE) or a stochastic differential equation (SDE), with discrete-time data is studied. A new estimation method is proposed based on minimizing the conditional least squares...

The small-sample performance of alternatives to the usual likelihood ratio test in mixed linear models is investigated. Specifically, the following tests for fixed effects are considered: (i) a bootstrap-based test, (ii) the Bartlett-corrected usual test, and (iii) an adjusted profile likelihood...

When there are many predictors, how to efficiently impute responses missing at random is an important problem to deal with for regression analysis because this missing mechanism, unlike missing completely at random, is highly related to high-dimensional predictor vectors. In sufficient dimension...