GLM permutation - nonparametric inference for arbitrary general linear models

Introduction: Permutation methods are finding growing use in neuroimag- ing data analyses (e.g. randomise in FSL, SnPM in SPM, XBAMM/BAMM/CAMBA, etc). These methods provide ex- act control of false positives, make only weak assumptions, and allow nonstandard types of statistics (e.g. smoothed variance t- test). With fast and inexpensive computing, there would seem few reasons not to use nonparametric methods. A significant limitation of these methods, however, is the lack of flexibility with respect to the experimental design and nuisance variables. Each specific design dictates the type of exchange- ability of null data, and hence how to permute. Nuisance effects (e.g. age) render data non-exchangeable even when the effect of interest is null. Hence, even something as simple as ANCOVA has no exact permutation test. Recently there has been an active literature on approximate– but accurate–permutation tests for 2-variable regression, one effect of interest, one nuisance (see review by Anderson & Robinson [1]). Here we extend and evaluate these methods for use with an arbitrary General Linear Model (GLM)

A timer inventory based upon manual and automated analysis of ERTS-1 and supporting aircraft data using multistage probability sampling

A quasi-operational study demonstrating that a timber inventory based on manual and automated analysis of ERTS-1, supporting aircraft data and ground data was made using multistage sampling techniques. The inventory proved to be a timely, cost effective alternative to conventional timber inventory techniques. The timber volume on the Quincy Ranger District of the Plumas National Forest was estimated to be 2.44 billion board feet with a sampling error of 8.2 percent. Costs per acre for the inventory procedure at 1.1 cent/acre compared favorably with the costs of a conventional inventory at 25 cents/acre. A point-by-point comparison of CALSCAN-classified ERTS data with human-interpreted low altitude photo plots indicated no significant differences in the overall classification accuracies

Stress Energy tensor in LCFT and the Logarithmic Sugawara construction

We discuss the partners of the stress energy tensor and their structure in Logarithmic conformal field theories. In particular we draw attention to the fundamental differences between theories with zero and non-zero central charge. However they are both characterised by at least two independent parameters. We show how, by using a generalised Sugawara construction, one can calculate the logarithmic partner of T. We show that such a construction works in the c=-2 theory using the conformal dimension one primary currents which generate a logarithmic extension of the Kac-Moody algebra.Comment: 19 pages. Minor correction

A feasibility study: California Department of Forestry and Fire Protection utilization of infrared technologies for wildland fire suppression and management

NASA's JPL has completed a feasibility study using infrared technologies for wildland fire suppression and management. The study surveyed user needs, examined available technologies, matched the user needs with technologies, and defined an integrated infrared wildland fire mapping concept system configuration. System component trade-offs were presented for evaluation in the concept system configuration. The economic benefits of using infrared technologies in fire suppression and management were examined. Follow-on concept system configuration development and implementation were proposed

Dynamic filtering of static dipoles in magnetoencephalography

We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem and model the neural current as a collection of evolving current dipoles, which appear and disappear, but whose locations are constant throughout their lifetime. This fully reflects the physiological interpretation of the model. In order to conduct inference under this proposed model, it was necessary to develop an algorithm based around state-of-the-art sequential Monte Carlo methods employing carefully designed importance distributions. Previous work employed a bootstrap filter and an artificial dynamic structure where dipoles performed a random walk in space, yielding nonphysical artefacts in the reconstructions; such artefacts are not observed when using the proposed model. The algorithm is validated with simulated data, in which it provided an average localisation error which is approximately half that of the bootstrap filter. An application to complex real data derived from a somatosensory experiment is presented. Assessment of model fit via marginal likelihood showed a clear preference for the proposed model and the associated reconstructions show better localisation

SMDS measurements and modeling to predict performance

The authors describe a performance study of a trial switched multimegabit data service (SMDS) link (intended for inter-LAN connection) from the perspective of customers evaluating the feasibility of the link for some target applications. The goals were to take all measurements on the customer premises and to develop a methodology general enough to be used by customers to evaluate the link. The authors measured a lightly loaded system and developed a model of the SMDS connection suitable for evaluating applications via analysis or simulation. They document their methodology and present the SMDS connection delay values as well as a likely breakdown of the constituents of that delay. They used these data to create a simulation model and to simulate a simple application. In the trial configuration, where geographical distances were small, SMDS network delay was one of the notable components of end-to-end delay in the SMDS connection. However, for most packets, throughput is limited by the T1 capacity for transmitting SMDS cells, not by the SMDS network capacity

Two-stage clustering in genotype-by-environment analyses with missing data

Cluster analysis has been commonly used in genotype-by-environment (G x E) analyses, but current methods are inadequate when the data matrix is incomplete. This paper proposes a new method, referred to as two-stage clustering, which relies on a partitioning of squared Euclidean distance into two independent components, the G x E interaction and the genotype main effect. These components are used in the first and second stages of clustering respectively. Two-stage clustering forms the basis for imputing missing values in the G x E matrix so that a more complete data array is available for other GxE analyses. Imputation for a given genotype uses information from genotypes with similar interaction profiles. This imputation method is shown to improve on an existing nearest cluster method that confounds the G x E interaction and the genotype main effect