30,698 research outputs found
Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners
The complexity of linear mixed-effects (LME) models means that traditional
diagnostics are rendered less effective. This is due to a breakdown of
asymptotic results, boundary issues, and visible patterns in residual plots
that are introduced by the model fitting process. Some of these issues are well
known and adjustments have been proposed. Working with LME models typically
requires that the analyst keeps track of all the special circumstances that may
arise. In this paper we illustrate a simpler but generally applicable approach
to diagnosing LME models. We explain how to use new visual inference methods
for these purposes. The approach provides a unified framework for diagnosing
LME fits and for model selection. We illustrate the use of this approach on
several commonly available data sets. A large-scale Amazon Turk study was used
to validate the methods. R code is provided for the analyses.Comment: 52 pages, 15 figures, 3 table
The ergonomics of command and control
Since its inception, just after the Second World War, ergonomics research has paid special attention to the issues surrounding human control of systems. Command and Control environments continue to represent a challenging domain for Ergonomics research. We take a broad view of Command and Control research, to include C2 (Command and Control), C3 (Command, Control and Communication), and C4 (Command, Control, Communication and Computers) as well as human supervisory control paradigms. This special issue of ERGONOMICS aims to present state-of-the-art research into models of team performance, evaluation of novel interaction technologies, case studies, methodologies and theoretical review papers. We are pleased to present papers that detail research on these topics in domains as diverse as the emergency services (e.g., police, fire, and ambulance), civilian applications (e.g., air traffic control, rail networks, and nuclear power) and military applications (e.g., land, sea and air) of command and control. While the domains of application are very diverse, many of the challenges they face share interesting similarities
Towards a Multi-Subject Analysis of Neural Connectivity
Directed acyclic graphs (DAGs) and associated probability models are widely
used to model neural connectivity and communication channels. In many
experiments, data are collected from multiple subjects whose connectivities may
differ but are likely to share many features. In such circumstances it is
natural to leverage similarity between subjects to improve statistical
efficiency. The first exact algorithm for estimation of multiple related DAGs
was recently proposed by Oates et al. 2014; in this letter we present examples
and discuss implications of the methodology as applied to the analysis of fMRI
data from a multi-subject experiment. Elicitation of tuning parameters requires
care and we illustrate how this may proceed retrospectively based on technical
replicate data. In addition to joint learning of subject-specific connectivity,
we allow for heterogeneous collections of subjects and simultaneously estimate
relationships between the subjects themselves. This letter aims to highlight
the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2
ConStance: Modeling Annotation Contexts to Improve Stance Classification
Manual annotations are a prerequisite for many applications of machine
learning. However, weaknesses in the annotation process itself are easy to
overlook. In particular, scholars often choose what information to give to
annotators without examining these decisions empirically. For subjective tasks
such as sentiment analysis, sarcasm, and stance detection, such choices can
impact results. Here, for the task of political stance detection on Twitter, we
show that providing too little context can result in noisy and uncertain
annotations, whereas providing too strong a context may cause it to outweigh
other signals. To characterize and reduce these biases, we develop ConStance, a
general model for reasoning about annotations across information conditions.
Given conflicting labels produced by multiple annotators seeing the same
instances with different contexts, ConStance simultaneously estimates gold
standard labels and also learns a classifier for new instances. We show that
the classifier learned by ConStance outperforms a variety of baselines at
predicting political stance, while the model's interpretable parameters shed
light on the effects of each context.Comment: To appear at EMNLP 201
Joint estimation of multiple related biological networks
Graphical models are widely used to make inferences concerning interplay in
multivariate systems. In many applications, data are collected from multiple
related but nonidentical units whose underlying networks may differ but are
likely to share features. Here we present a hierarchical Bayesian formulation
for joint estimation of multiple networks in this nonidentically distributed
setting. The approach is general: given a suitable class of graphical models,
it uses an exchangeability assumption on networks to provide a corresponding
joint formulation. Motivated by emerging experimental designs in molecular
biology, we focus on time-course data with interventions, using dynamic
Bayesian networks as the graphical models. We introduce a computationally
efficient, deterministic algorithm for exact joint inference in this setting.
We provide an upper bound on the gains that joint estimation offers relative to
separate estimation for each network and empirical results that support and
extend the theory, including an extensive simulation study and an application
to proteomic data from human cancer cell lines. Finally, we describe
approximations that are still more computationally efficient than the exact
algorithm and that also demonstrate good empirical performance.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS761 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Automatic Detection of Outliers in Multibeam Echo Sounding Data
The data volumes produced by new generation multibeam systems are very large, especially for shallow water systems. Results from recent multibeam surveys indicate that the ratio of the field survey time, to the time used in interactive editing through graphical editing tools, is about 1:1. An important reason for the large amount of processing time is that users subjectively decide which soundings are outliers. There is an apparent need for an automated approach for detecting outliers that would reduce the extensive labor and obtain consistent results from the multibeam data cleaning process, independent of the individual that has processed the data. The proposed automated algorithm for cleaning multibeam soundings was tested using the SAX-99 (Destin FL) multibeam survey data [2]. Eight days of survey data (6.9 Gigabyte) were cleaned in 2.5 hours on an SGI platform. A comparison of the automatically cleaned data with the subjective, interactively cleaned data indicates that the proposed method is, if not better, at least equivalent to interactive editing as used on the SAX-99 multibeam data. Furthermore, the ratio of acquisition to processing time is considerably improved since the time required for cleaning the data was decreased from 192 hours to 2.5 hours (an improvement by a factor of 77)
Decision paths in complex tasks
Complex real world action and its prediction and control has escaped analysis by the classical methods of psychological research. The reason is that psychologists have no procedures to parse complex tasks into their constituents. Where such a division can be made, based say on expert judgment, there is no natural scale to measure the positive or negative values of the components. Even if we could assign numbers to task parts, we lack rules i.e., a theory, to combine them into a total task representation. We compare here two plausible theories for the amalgamation of the value of task components. Both of these theories require a numerical representation of motivation, for motivation is the primary variable that guides choice and action in well-learned tasks. We address this problem of motivational quantification and performance prediction by developing psychophysical scales of the desireability or aversiveness of task components based on utility scaling methods (Galanter 1990). We modify methods used originally to scale sensory magnitudes (Stevens and Galanter 1957), and that have been applied recently to the measure of task 'workload' by Gopher and Braune (1984). Our modification uses utility comparison scaling techniques which avoid the unnecessary assumptions made by Gopher and Braune. Formula for the utility of complex tasks based on the theoretical models are used to predict decision and choice of alternate paths to the same goal
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Bayesian belief network model for the safety assessment of nuclear computer-based systems
The formalism of Bayesian Belief Networks (BBNs) is being increasingly applied to probabilistic modelling and decision problems in a widening variety of fields. This method provides the advantages of a formal probabilistic model, presented in an easily assimilated visual form, together with the ready availability of efficient computational methods and tools for exploring model consequences. Here we formulate one BBN model of a part of the safety assessment task for computer and software based nuclear systems important to safety. Our model is developed from the perspective of an independent safety assessor who is presented with the task of evaluating evidence from disparate sources: the requirement specification and verification documentation of the system licensee and of the system manufacturer; the previous reputation of the various participants in the design process; knowledge of commercial pressures;information about tools and resources used; and many other sources. Based on these multiple sources of evidence, the independent assessor is ultimately obliged to make a decision as to whether or not the system should be licensed for operation within a particular nuclear plant environment. Our BBN model is a contribution towards a formal model of this decision problem. We restrict attention to a part of this problem: the safety analysis of the Computer System Specification documentation. As with other BBN applications we see this modelling activity as having several potential benefits. It employs a rigorous formalism as a focus for examination, discussion, and criticism of arguments about safety. It obliges the modeller to be very explicit about assumptions concerning probabilistic dependencies, correlations, and causal relationships. It allows sensitivity analyses to be carried out. Ultimately we envisage this BBN, or some later development of it, forming part of a larger model, which might well take the form of a larger BBN model, covering all sources of evidence about pre-operational life-cycle stages. This could provide an integrated model of all aspects of the task of the independent assessor, leading up to the final judgement about system safety in a particular context. We expect to offer some results of this further work later in the DeVa project
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