Jamshidian and Zhu (1997) propose a discrete grid method for simplifying the computation of Value at Risk (VaR) for fixed-income portfolios. Their method relies on two simplifications. First, the value of fixed income instruments is modeled as depending on a small number of risk factors chosen using principal components analysis. Second, they use a discrete approximation to the distribution of ...

In this paper, we propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, int...

Simple point-optimal sign-based tests are developed for inference on linear and nonlinear regression models with non-Gaussian heteroskedastic errors. The tests are exact, distribution-free, robust to heteroskedasticity of unknown form, and may be inverted to build confidence regions for the parameters of the regression function. Since point-optimal sign tests depend on the alternative hypothesi...

PLS path modeling (Partial Least Squares path modeling) has found increased interests since being used in the context of marketing studies. In this paper, we use Monte Carlo simulation and real life datasets to investigate the effects of changes in the model specification or in the data. We first introduce PLS path modeling and some already well-known properties of this approach; we then conduc...

Monte Carlo experiments aimed at assessing the statistical power of structural equation modeling (SEM) techniques typically focus on true population path coefficients, ignoring true sample path coefficients. We demonstrate the limitations stemming from such practice in statistical power assessments. This is done in the context of SEM techniques employing the partial least squares (PLS) method, ...

We consider the problem of estimating continuous-time autoregressive (CAR) processes from discrete-time noisy observations. This can be done within a Bayesian framework using Markov chain Monte Carlo (MCMC) methods. Existing methods include the standard random walk Metropolis algorithm. On the other hand, least-squares (LS) algorithms exist where derivatives are approximated by di erences and p...

In this paper we develop an asymptotic theory for estimation based on signed ranks in the ARMA model when the innovation density is symmetrical. We provide two classes of estimators and we establish their asymptotic normality with the help of the asymptotic properties for serial signed rank statistics. Finally, we compare our procedure to the one of least-squares, and we illustrate the performa...

The most fundamental problem currently associated with structural equation modeling employing the partial least squares method is that it does not properly account for measurement error, which often leads to path coefficient estimates that asymptotically converge to values of lower magnitude than the true values. This attenuation phenomenon affects applications in the field of business data ana...