Master of Science / Department of Statistics / Paul I. Nelson / The Calibration-Inverse Prediction Problem was investigated in a mixed model setting. Two methods were used to construct inverse prediction intervals. Method 1 ignores the random block effect in the mixed model and constructs the inverse prediction interval in the standard manner using quantiles from an F distribution. Method 2 uses a bootstrap to estimate quantiles of an approximate pivotal and then follows essentially the same procedure as in method 1. A simulation study was carried out to compare how the intervals created by the two methods performed in terms of coverage rate and mean interval length. Results from our simulation study suggest that when the variance component of the block is large relative to the location variance component, the coverage rate of the intervals produced by the two methods differ significantly. Method 2 appears to yield intervals which have a slightly higher coverage rate and wider interval length then did method 1. Both methods yielded intervals with coverage rates below nominal for approximately 1/3 of the simulation settings.

This thesis summarizes latest findings about municipal solid waste (MSW) modelling. These are used to solve multivariable version of inverse prediction problem. It is not possible to solve such problem analytically, so heuristic framework using regression models and data reconciliation was developed. As a side product, models for MSW modelling using PCA (Principal Component Analysis) and LM (Linear Model) were created. These were compared with heuristic model called RF (Random Forest). Both of these models were also used for per capita MSW modelling. Theoretical parts about generalized linear models, data reconciliation and nonlinear programming are also included.