Due to the structure of the solution set an exact solution to... bounds are incorporated into the model to prevent the computed solution from violating the bound constraints. Theory in the literature is extended to the new model. Empirical evidence shows that the proposed model reduces the number of projection attempts needed to find an exact solution. When early termination is the goal, projection from a pure composite Newton step is advocated. However, the cost may exceed the benefits due to the average need of more than one projection attempt to find an exact solution. Variants of Mehrotra's predictor-corrector primal-dual interior-point algorithm provide the foundation for most practical interior-point codes. To take advantage of all available algorithmic information, we investigate the behavior of the Tapia predictor-corrector indicator, which incorporates the corrector step, to identify the optimal partition. Globally, the Tapia predictor-corrector indicator behaves poorly as do all indicators, but locally exhibits fast convergence.

There are several models that predict ambient (ground level) ozone pollution by transforming the data. However, transformations sometime increase prediction error. Our goal is to directly model ambient ozone levels in regions located away from monitoring stations. Our objective is not to predict future ozone levels. Some Key Words: Multivariate skew-normal distribution; Bivariate skew-normal distribution; Ambient ozone. __________________________________ * Research supported by the Center for Research and Parallel Computation, Rice University, Houston, Texas ** Graduate Student, Department of Statistics, Rice University, Houston, Texas Introduction Ambient ozone (O 3 ) pollution is a major environmental problem in many metropolitan areas in this country. In particular, the Houston, Texas area is second to Los Angeles in nationwide ambient air quality (Carroll, Chen, George, Li, Newton, Schmiediche and Wang, 1997). There are several models for estimating O 3 , however, many modeling...