Hermite regression analysis of multi-modal count data

We discuss the modeling of count data whose empirical distribution is both multi-modal and over-dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models.Count data, multi-modal data, over-dispersion, financial crises

Reducing the bias of the maximum likelihood estimator for the Poisson regression model

We derive expressions for the first-order bias of the MLE for a Poisson regression model and show how these can be used to adjust the estimator and reduce bias without increasing MSE. The analytic results are supported by Monte Carlo simulations and three illustrative empirical applications.Poisson regression, maximum likelihood estimation, bias reduction

Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes

When solving stochastic partial differential equations (SPDEs) driven by additive spatial white noise, the efficient sampling of white noise realizations can be challenging. Here, we present a new sampling technique that can be used to efficiently compute white noise samples in a finite element method and multilevel Monte Carlo (MLMC) setting. The key idea is to exploit the finite element matrix assembly procedure and factorize each local mass matrix independently, hence avoiding the factorization of a large matrix. Moreover, in a MLMC framework, the white noise samples must be coupled between subsequent levels. We show how our technique can be used to enforce this coupling even in the case of non-nested mesh hierarchies. We demonstrate the efficacy of our method with numerical experiments. We observe optimal convergence rates for the finite element solution of the elliptic SPDEs of interest in 2D and 3D and we show convergence of the sampled field covariances. In a MLMC setting, a good coupling is enforced and the telescoping sum is respected.Comment: 28 pages, 10 figure

Gene-knockdown in the honey bee mite Varroa destructor by a non-invasive approach : studies on a glutathione S-transferase

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Effects of nonlinear aerodynamics and static aeroelasticity on mission performance calculations for a fighter aircraft

During conceptual design studies of advanced aircraft, the usual practice is to use linear theory to calculate the aerodynamic characteristics of candidate rigid (nonflexible) geometric external shapes. Recent developments and improvements in computational methods, especially computational fluid dynamics (CFD), provide significantly improved capability to generate detailed analysis data for the use of all disciplines involved in the evaluation of a proposed aircraft design. A multidisciplinary application of such analysis methods to calculate the effects of nonlinear aerodynamics and static aeroelasticity on the mission performance of a fighter aircraft concept is described. The aircraft configuration selected for study was defined in a previous study using linear aerodynamics and rigid geometry. The results from the previous study are used as a basis of comparison for the data generated herein. Aerodynamic characteristics are calculated using two different nonlinear theories, potential flow and rotational (Euler) flow. The aerodynamic calculations are performed in an iterative procedure with an equivalent plate structural analysis method to obtain lift and drag data for a flexible (nonrigid) aircraft. These static aeroelastic data are then used in calculating the combat and mission performance characteristics of the aircraft

Steric engineering of metal-halide perovskites with tunable optical band gaps

Owing to their high energy-conversion efficiency and inexpensive fabrication routes, solar cells based on metal-organic halide perovskites have rapidly gained prominence as a disruptive technology. An attractive feature of perovskite absorbers is the possibility of tailoring their properties by changing the elemental composition through the chemical precursors. In this context, rational in silico design represents a powerful tool for mapping the vast materials landscape and accelerating discovery. Here we show that the optical band gap of metal-halide perovskites, a key design parameter for solar cells, strongly correlates with a simple structural feature, the largest metal-halide-metal bond angle. Using this descriptor we suggest continuous tunability of the optical gap from the mid-infrared to the visible. Precise band gap engineering is achieved by controlling the bond angles through the steric size of the molecular cation. Based on these design principles we predict novel low-gap perovskites for optimum photovoltaic efficiency, and we demonstrate the concept of band gap modulation by synthesising and characterising novel mixed-cation perovskites.Comment: This manuscript was submitted for publication on March 6th, 2014. Many of the results presented in this manuscript were presented at the International Conference on Solution processed Semiconductor Solar Cells, held in Oxford, UK, on 10-12 September 2014. The manuscript is 37 pages long and contains 8 figure

Transcriptome analysis of the synganglion from the honey bee mite, Varroa destructor and RNAi knockdown of neural peptide targets

Acknowledgements This work was funded by BBSRC-LINK grant # BB/J01009X/1 and Vita Europe Ltd. We are grateful to the Scottish Beekeepers Association, especially Mr Phil McAnespie in supporting this work at its inception. We acknowledge partial funding from a Genesis Faraday SPARK Award, part of a Scottish Government SEEKIT project for the early part of this work. We are grateful to Prof David Evans for his advice on Varroa destructor viruses.Peer reviewedPostprin