Bias Reduction using Stochastic Approximation

The paper studies stochastic approximation as a technique for bias reduction. The proposed method does not require approximating the bias explicitly, nor does it rely on having independent identically distributed (i.i.d.) data. The method always removes the leading bias term, under very mild conditions, as long as auxiliary samples from distributions with given parameters are available. Expectation and variance of the bias-corrected estimate are given. Examples in sequential clinical trials (non-i.i.d. case), curved exponential models (i.i.d. case) and length-biased sampling (where the estimates are inconsistent) are used to illustrate the applications of the proposed method and its small sample properties.     

A grounded theory research approach to building and testing TQM theory in operations management

Total quality management (TQM) has continued to develop as a strategic business improvement approach in organisations and within the Operations Management literature. Strategic TQM is a dynamic phenomenon, reflecting the complexity and technology development in the business environment. Therefore, this conception of TQM has led to significant challenges with regards to developing suitable models and research methodology where traditional, and normative research data, includes survey responses associated with deductive theory and testing. Such data, and its use, is often premised on cause and effect rationality and fails to supply deep rich data to address meanings, phenomena and complex socio-political events, which is a feature of strategic TQM.     

Capturing touch in parent–infant interaction: A comparison of methods

Naturally occurring high levels of caregiver touch promote offspring development in many animal species. Yet, caregiver touch remains a relatively understudied topic in human development, possibly due to challenges of measuring this means of interaction. While parental reports (e.g., questionnaires, diaries) are easy to collect, they may be subject to biases and memory limitations. In contrast, observing touch in a short session of parent–child interaction in the lab may not be representative of touch interaction in daily life. In the present study, we compared parent reports (one-off questionnaires and diary) and observation-based methods in a sample of German 6- to 13-month-olds and their primary caregivers (n = 71). In an attempt to characterize touching behaviors across a broad range of contexts, we measured touch both during play and while the parent was engaged in another activity. We found that context affected both the quantity and types of touch used in interaction. Parent-reported touch was moderately associated with touch observed in parent–child interactions and more strongly with touch used during play. We conclude that brief one-off questionnaires are a good indicator of touch in parent–child interaction, yet they may be biased toward representing particular daily activities and particular types of touch.     

Consistent procedures for multiclass classification of discrete diffusion paths

The recent advent of modern technology has generated a large number of datasets which can be frequently modeled as functional data. This paper focuses on the problem of multiclass classification for stochastic diffusion paths. In this context we establish a closed formula for the optimal Bayes rule. We provide new statistical procedures which are built either on the plug-in principle or on the empirical risk minimization principle. We show the consistency of these procedures under mild conditions. We apply our methodologies to the parametric case and illustrate their accuracy with a simulation study through examples.     

Stochastically Modeling Listeria Monocytogenes Growth in Farm Tank Milk

This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.