Optimal bandwidths for local polynomial regression usually involve functionals of the derivatives of the unknown regression function. In the multivariate case, estimates of these functionals are not readily available, primarily because estimating multivariate derivatives is complicated. In this paper, an estimator of multivariate second derivative is obtained via local quadratic regression with...

In some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. In this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribu...

In some statistical process control applications, the process data are not Normally distributed and characterized by the combination of both variable and attributes quality characteristics. Despite different methods which are proposed separately for monitoring multivariate and multi-attribute processes, only few methods are available in the literature for monitoring multivariate-attribute proce...

many health studies were interested to find the association between multiple predictors with a dependent variable. the popular method to analyze these studies was multiple regressions that assumed the predictors were independent. while in practice some predictors may depend on each other and they could not be presented in a regression model simultaneously, as a result the appropriate models suc...

i ABSTRACT There has been much research involving simultaneous monitoring of several correlated quality characteristics that rely on the assumptions of multivariate normality and independence. In real world applications, these assumptions are not always met, particularly when small counts are of interest. In general, the use of normal approximation to the Poisson distribution seems to be justif...

We first describe a globally convergent matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. Then, we apply this approximation procedure to the estimation of multivariate spectral densities, and test its effectiveness through simulation.

In this paper, the conservative Monte Carlo error estimation methods and theory developed in Geyer (1992a) are extended from univariate to multivariate Markov chain applications. A small simulation study demonstrates the feasibility of the proposed estimators.