Modelling of errors in databases

A lot of time and energy are expended assembling national databases containing information about health care processes and outcomes. Unfortunately, given the complexity of the data gathering procedures involved, errors occur. This inevitably leads to problems when it comes to the analysis of data from such sources. Indeed, sometimes it is very much a matter of faith that summary statistics represent a true reflection of the facts. On the assumption that one knows the rates at which different forms of errors occur, mathematical modelling methods can be used to obtain estimates of the effects of such errors on the estimates that would be derived for summary statistics associated with an erroneous data base

The thermal simulation of an office building implementing a new behavioural algorithm for window opening and the use of ceiling fans

This investigation of the window opening data from extensive field surveys in UK office buildings investigates 1) how people control the indoor environment by opening windows, 2) the cooling potential of opening windows, and 3) the use of an “adaptive algorithm” for predicting window opening behaviour for thermal simulation in ESP-r. We found that the mean indoor and outdoor temperatures when the window was open were higher than when it was closed, but show that nonetheless there was a useful cooling effect from opening a window. The adaptive algorithm for window opening behaviour was then used in thermal simulation studies for some typical office designs. The thermal simulation results were in general agreement with the findings of the field surveys

Work Roll Cooling System Design Optimisation in Presence of Uncertainty

Organised by: Cranfield UniversityThe paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops Meta models from a set of Finite Element Analysis (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective algorithm is applied to the design optimisation I order to identify a set of good solutions in the presence of uncertainties both in the decision and objective spaces.Mori Seiki – The Machine Tool Compan

A window opening algorithm and UK office temperature field results and thermal simulation

This investigation of the window opening data from extensive field surveys in UK office buildings investigates 1) how people control the indoor environment by opening windows, 2) the cooling potential of opening windows, and 3) the use of an “adaptive algorithm” for predicting window opening behaviour for thermal simulation in ESP-r. We found that the mean indoor and outdoor temperatures when the window was open were higher than when it was closed, but show that nonetheless there was a useful cooling effect from opening a window. The adaptive algorithm for window opening behaviour was then used in thermal simulation studies for some typical office designs. The thermal simulation results were in general agreement with the findings of the field surveys

Estimation of nonlinear models with Berkson measurement errors

This paper is concerned with general nonlinear regression models where the predictor variables are subject to Berkson-type measurement errors. The measurement errors are assumed to have a general parametric distribution, which is not necessarily normal. In addition, the distribution of the random error in the regression equation is nonparametric. A minimum distance estimator is proposed, which is based on the first two conditional moments of the response variable given the observed predictor variables. To overcome the possible computational difficulty of minimizing an objective function which involves multiple integrals, a simulation-based estimator is constructed. Consistency and asymptotic normality for both estimators are derived under fairly general regularity conditions.Comment: Published at http://dx.doi.org/10.1214/009053604000000670 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org