Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction

We consider the problem of bandwidth selection by cross-validation from a sequential point of view in a nonparametric regression model. Having in mind that in applications one often aims at estimation, prediction and change detection simultaneously, we investigate that approach for sequential kernel smoothers in order to base these tasks on a single statistic. We provide uniform weak laws of large numbers and weak consistency results for the cross-validated bandwidth. Extensions to weakly dependent error terms are discussed as well. The errors may be {\alpha}-mixing or L2-near epoch dependent, which guarantees that the uniform convergence of the cross validation sum and the consistency of the cross-validated bandwidth hold true for a large class of time series. The method is illustrated by analyzing photovoltaic data.Comment: 26 page

A Binning Approach to Quickest Change Detection with Unknown Post-Change Distribution

The problem of quickest detection of a change in distribution is considered under the assumption that the pre-change distribution is known, and the post-change distribution is only known to belong to a family of distributions distinguishable from a discretized version of the pre-change distribution. A sequential change detection procedure is proposed that partitions the sample space into a finite number of bins, and monitors the number of samples falling into each of these bins to detect the change. A test statistic that approximates the generalized likelihood ratio test is developed. It is shown that the proposed test statistic can be efficiently computed using a recursive update scheme, and a procedure for choosing the number of bins in the scheme is provided. Various asymptotic properties of the test statistic are derived to offer insights into its performance trade-off between average detection delay and average run length to a false alarm. Testing on synthetic and real data demonstrates that our approach is comparable or better in performance to existing non-parametric change detection methods.Comment: Double-column 13-page version sent to IEEE. Transaction on Signal Processing. Supplementary material include

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstracts

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstract

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstracts

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstract

Spatially Indexed Functional Data

The increased concentration of greenhouse gases is associated with the global warming in the lower troposphere. For over twenty years, the space physics community has studied a hypothesis of global cooling in the thermosphere, attributable to greenhouse gases. While the global temperature increase in the lower troposphere has been relatively well established, the existence of global changes in the thermosphere is still under investigation. A central difficulty in reaching definite conclusions is the absence of data with sufficiently long temporal and sufficiently broad spatial coverage. Time series of data that cover several decades exist only in a few separated regions. The space physics community has struggled to combine the information contained in these data, and often contradictory conclusions have been reported based on the analyses relying on one or a few locations. To detect global changes in the ionosphere, we present a novel statistical methodology that uses all data, even those with incomplete temporal coverage. It is based on a new functional regression approach that can handle unevenly spaced, partially observed curves. While this research makes a solid contribution to the space physics community, our statistical methodology is very flexible and can be useful in other applied problems

Analysis of Eye Tracking Data to Measure Situational Awareness in Offshore Drilling Operations

In complex, high-stakes tasks such as offshore oil and gas drilling where substantial number of monitoring parameters involve in the operation, the analyses of human operator’s situational awareness (or situation awareness, SA) become more important to avoid severe incidents initiated by the poor cognitive performance. Numerous SA measurement practices have been proposed in the previous researches, however, most of them employed the verbal and behavioral response analyses which are subjective to the researchers’ notions. In this study, an integrated approach combining subjective measures (e.g. verbal responses) with physiological metrics (e.g. eye fixation data) was investigated to seek the benefits for SA analyses in the field of offshore oil and gas drilling. A pre-existing incident based experimental test in a high-fidelity simulator facility was designed for real-time log indicators monitoring tasks, and a set of eye tracking devices collected verbal responses and oculomotor information simultaneously during the real-time tasks. To quantify the verbal responses, scoring metrics were newly developed for this study. The metrics assigned the points to the participants’ verbal responses based on their uttered keywords (abnormal, kick or blow-out) reacting to the situation. Quantitative statistical analyses were applied to ocular observations and verbal response scores collected from the predesigned Areas of Interests (AOIs) on the monitoring screen, using one-way ANOVA and Friedman test, respectively. The analyses provided unique and complementary insights that mapped the measures from both metrics to the level of situation awareness and that helped understand the cognitive process in time critical decision-making tasks in offshore oil and gas field. In addition to the statistical investigation, data mining approach using time series clustering technique was introduced to group the participants’ scanning pattern with respect to the temporal sequences, and to find the correlation of the scanning pattern to the quantified situation awareness. According to the analysis results, the expertise of the participants affected on their cognitive mechanisms to identify and respond to the situations to some extent. The content and timing of the situation also served as one of the important factors to determine the level of situation awareness. The participants’ scan patterns were clustered into four groups and suggested a potential correlation between the visual scanning pattern and the quantified situation awareness (i.e. verbal response scores). It was found that the vertical attending tendencies to the individual logs might lead a higher comprehension of the situation than the horizontally transitional attending tendencies between different logs

Quantitative 3D orientation analysis of particles and voids to differentiate hand-built pottery forming techniques using X-ray microtomography and neutron tomography

This article describes the quantitative analysis of the 3D orientation of objects (i.e. particles and voids) within pottery fabrics to differentiate two categories of pottery hand-building primary forming techniques, specifically percussion-building and coil-building, comparing the use of two independent non-destructive imaging modalities, X-ray microtomography (µ-CT) and neutron tomography (NT). For this purpose, series of experimental organic-tempered vessels and coil sections were analysed. For both imaging modalities, two separate systems were employed for quantitatively describing both the orientation of individual objects, as well as the collective preferential alignment of objects within samples, utilising respectively polar and azimuth angles within a spherical coordinate system, and projected sizes within a positive Cartesian coordinate system. While the former provided full descriptions of the orientations of objects within 3D space, the latter, through a ratio dubbed here the ‘Orientation Index’ (OI), gave a simple numerical value with which the investigated samples were differentiated according to forming technique. Both imaging modalities were able to differentiate between coil-built and percussion-built vessels with a high degree of confidence, with the strength of these findings additionally demonstrated through extensive statistical modelling using Monte Carlo simulations. Despite differences in resolution and differences in the attenuation of X-rays and neutrons, µ-CT and NT were shown to provide comparable results. The findings presented here broadly agree with earlier studies; however, the quantitative and three-dimensional nature of the results enables more subtle features to be identified, while additionally, in principle, the non-destructive nature of both imaging techniques facilitates such structural analysis without recourse to invasive sampling

Robust statistical approaches for feature extraction in laser scanning 3D point cloud data

Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction