A Method for Ascertaining and Controlling Representation Bias in Field Trials for Airborne Plant Pathogens

The basic premise of running a field trial is that the estimates of treatment effects obtained are representative of how the different treatments will perform in the field. The disparities between the treatment effects observed experimentally, and those that would be observed were the treatments applied to the field, we term 'representation bias.' When looking at field trials testing the efficacies of treatment sprays on plant pathogens, representation bias can be caused by positive and negative inter-plot interference. The potential for such effects will be greatest when looking at pathogens that are dispersed by wind. In this paper, a computer simulation that simulates plant disease dispersal under such conditions is described. This program is used to quantify the amount of representation bias occurring in various experimental situations. Through this, the relationships between field design parameters and representation bias are explored, and the importance of plot dimension and spacing, as well as treatment to plot allocation, emphasized.     

Designs to guard against outliers in the présence or absence of model bias

Optimality of experimental design is considered in the situation in which individual observations may be subject to a shift in mean. The criterion of minimum average integrated mean square error is examined in general and the consequences for first and second degree models in the présence or absence of model bias are discussed.     

Crossover designs for comparing test treatments to a control treatment when subject effects are random

We study crossover designs for the comparisons of several test treatments versus a control treatment and partially generalize the results of Hedayat and Yang (2005) to the situation in which subject effects are assumed to be random. More specifically, we establish lower bounds for the trace of the inverse of the information matrix for the test treatments versus control comparisons under a random subject effects model and show that most of the small size (3-, 4- and 5-period) designs introduced by Hedayat and Yang (2005) are highly efficient in the class of designs in which the control treatment appears equally often in all periods and no treatment is immediately preceded by itself.     

Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

Simulation-based model comparison methodology with application to road accident models

This article assumes the goal of proposing a simulation-based theoretical model comparison methodology with application to two time series road accident models. The model comparison exercise helps to quantify the main differences and similarities between the two models and comprises of three main stages: (1) simulation of time series through a true model with predefined properties; (2) estimation of the alternative model using the simulated data; (3) sensitivity analysis to quantify the effect of changes in the true model parameters on alternative model parameter estimates through analysis of variance, ANOVA. The proposed methodology is applied to two time series road accident models: UCM (unobserved components model) and DRAG (Demand for Road Use, Accidents and their Severity). Assuming that the real data-generating process is the UCM, new datasets approximating the road accident data are generated, and DRAG models are estimated using the simulated data. Since these two methodologies are usually assumed to be equivalent, in a sense that both models accurately capture the true effects of the regressors, we are specifically addressing the modeling of the stochastic trend, through the alternative model. Stochastic trend is the time-varying component and is one of the crucial factors in time series road accident data. Theoretically, it can be easily modeled through UCM, given its modeling properties. However, properly capturing the effect of a non-stationary component such as stochastic trend in a stationary explanatory model such as DRAG is challenging. After obtaining the parameter estimates of the alternative model (DRAG), the estimates of both true and alternative models are compared and the differences are quantified through experimental design and ANOVA techniques. It is observed that the effects of the explanatory variables used in the UCM simulation are only partially captured by the respective DRAG coefficients. This a priori, could be due to multicollinearity but the results of both simulation of UCM data and estimating of DRAG models reveal that there is no significant static correlation among regressors. Moreover, in fact, using ANOVA, it is determined that this regression coefficient estimation bias is caused by the presence of the stochastic trend present in the simulated data. Thus, the results of the methodological development suggest that the stochastic component present in the data should be treated accordingly through a preliminary, exploratory data analysis.