Partial Least Squares Regression (PLSR) is a method for constructing predictive models when the variables are many and highly collinear. Its goal is to predict a set of response variables from a set of predictor variables. This prediction is achieved by extracting a set of orthogonal factors called latent variables from the predictor variables. This study investigated the performances of model ...

This paper addresses two issues. Firstly, because the coarse­scale model inevitably misses out sub­grid heterogeneity, physical dispersion is ignored in the simulation. Secondly, the small­scale heterogeneity is not explicitly known and can only be inferred by history­matching. To solve these problems, local features in the coarse­scale relative permeability curves were adjusted in history­matc...

Conventional molecular dynamics simulations macromolecules require long computational times because the most interesting motions are very slow compared with the fast oscillations of bond lengths and bond angles that limit the integration time step. Simulation of dynamics in the space of internal coordinates, that is with bond lengths, bond angles and torsions as independent variables, gives a t...

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the co...

This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate. The sample consists of a dependent variable and a set of covariates, one of which is discrete and arbitrarily correlates with the unobserved covariate. The observed discrete covariate has the same support as the unobserved covariate, and can be interpreted as a proxy or ...

The restricted measurement error model is employed when certain study variables are not observable by direct measurement and if some information about the unknown regression coefficients is available a priori. In this study, we present a method for checking the goodness of fit in the restricted measurement error model. We obtain the goodness-offit statistics based on the concept of coefficient ...

This paper develops a bias correction scheme for a multivariate normal model under a general parameterization. In the model, the mean vector and the covariance matrix share the same parameters. It includes many important regression models available in the literature as special cases, such as (non)linear regression, errors-in-variables models, and so forth. Moreover, heteroscedastic situations m...

Asymptotic expansions of the distributions of typical estimators in canonical correlation analysis under nonnormality are obtained. The expansions include the Edgeworth expansions up to order O(1/n) for the parameter estimators standardized by the population standard errors, and the corresponding expansion by Hall’s method with variable transformation. The expansions for the Studentized estimat...

Ensuring the reliability of multithreaded software systems is difficult due to the potential for subtle interactions between threads. Unfortunately, checking tools for such systems do not scale to programs with a large number of threads and procedures. To improve this shortcoming, we present a verification technique that uses concise specifications to analyze large multithreaded programs modula...

An approximate analytical approach to describe the stochastic motion of sound rays in deep ocean is developed. This is done for a realistic propagation model with an internal wave induced perturbation imposed on the smooth background sound speed field. The chaotic ray dynamics is analyzed using the Hamiltonian formalism taken in terms of the action-angle canonical variables. It is shown that ev...