Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Effect of polymer concentration on stabilized large-tilt-angle flexoelectro-optic switching

In this letter, the uniform lying helix (ULH) liquid crystal texture, required for the flexoelectro-optic effect, is polymer stabilized by the addition of a small percentage of reactive mesogen to a high-tilt-angle (φ>60°) bimesogenic chiral nematic host. The electro-optic response is measured for a range of reactive mesogen concentration mixtures, and compared to the large-tilt-angle switch of the pure chiral nematic mixture. The optimum concentration of reactive mesogen, which is found to provide ample stabilization of the texture with minimal impact on the electro-optic response, is found to be approximately 3%. Our results indicate that polymer stabilization of the ULH texture using a very low concentration of reactive mesogen is a reliable way of ruggedizing flexoelectro-optic devices without interfering significantly with the electro-optics of the effect, negating the need for complicated surface alignment patterns or surface-only polymerization. The polymer stabilization is shown to reduce the temperature dependence of the flexoelectro-optic response due to "pinning" of the chiral nematic helical pitch. This is a restriction of the characteristic thermochromic behavior of the chiral nematic. Furthermore, selection of the temperature at which the sample is ultraviolet cured allows the tilt angle to be optimized for the entire chiral nematic temperature range. The response time, however, remains more sensitive to operating temperature than curing temperature. This allows the sample to be cured at low temperature and operated at high temperature, providing simultaneous optimization of these two previously antagonistic performance aspects. © 2006 American Institute of Physics.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.