L2L_2 boosting in kernel regression

In this paper, we investigate the theoretical and empirical properties of $L_2$ boosting with kernel regression estimates as weak learners. We show that each step of $L_2$ boosting reduces the bias of the estimate by two orders of magnitude, while it does not deteriorate the order of the variance. We illustrate the theoretical findings by some simulated examples. Also, we demonstrate that $L_2$ boosting is superior to the use of higher-order kernels, which is a well-known method of reducing the bias of the kernel estimate.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ160 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Generalised additive dependency inflated models including aggregated covariates

Let us assume that X, Y and U are observed and that the conditional mean of U given X and Y can be expressed via an additive dependency of X, λ(X)Y and X + Y for some unspecified function . This structured regression model can be transferred to a hazard model or a density model when applied on some appropriate grid, and has important forecasting applications via structured marker dependent hazards models or structured density models including age-period-cohort relationships. The structured regression model is also important when the severity of the dependent variable has a complicated dependency on waiting times X, Y and the total waiting time X+Y . In case the conditional mean of U approximates a density, the regression model can be used to analyse the age-period-cohort model, also when exposure data are not available. In case the conditional mean of U approximates a marker dependent hazard, the regression model introduces new relevant age-period-cohort time scale interdependencies in understanding longevity. A direct use of the regression relationship introduced in this paper is the estimation of the severity of outstanding liabilities in non-life insurance companies. The technical approach taken is to use B-splines to capture the underlying one-dimensional unspecified functions. It is shown via finite sample simulation studies and an application for forecasting future asbestos related deaths in the UK that the B-spline approach works well in practice. Special consideration has been given to ensure identifiability of all models considered

Bacteriorhodopsin folds through a poorly organized transition state.

The folding mechanisms of helical membrane proteins remain largely uncharted. Here we characterize the kinetics of bacteriorhodopsin folding and employ φ-value analysis to explore the folding transition state. First, we developed and confirmed a kinetic model that allowed us to assess the rate of folding from SDS-denatured bacteriorhodopsin (bRU) and provides accurate thermodynamic information even under influence of retinal hydrolysis. Next, we obtained reliable φ-values for 16 mutants of bacteriorhodopsin with good coverage across the protein. Every φ-value was less than 0.4, indicating the transition state is not uniquely structured. We suggest that the transition state is a loosely organized ensemble of conformations

In good company: risk, security and choice in young people's drug decisions

This article draws on original empirical research with young people to question the degree to which 'individualisation of risk', as developed in the work of Beck and Giddens, adequately explains the risks young people bear and take. It draws on alternative understandings and critiques of 'risk' not to refute the notion of the reflexive individual upon which 'individualisation of risk' is based but to re-read that reflexivity in a more hermeneutic way. It explores specific risk-laden moments – young people's drug use decisions – in their natural social and cultural context of the friendship group. Studying these decisions in context, it suggests, reveals the meaning of 'risk' to be not given, but constructed through group discussion, disagreement and consensus and decisions taken to be rooted in emotional relations of trust, mutual accountability and common security. The article concludes that 'the individualisation of risk' fails to take adequate account of the significance of intersubjectivity in risk-decisions. It argues also that addressing the theoretical overemphasis on the individual bearer of risk requires not only further empirical testing of the theory but appropriate methodological reflection

Regularization, Renormalization and Range: The Nucleon-Nucleon Interaction from Effective Field Theory

Regularization and renormalization is discussed in the context of low-energy effective field theory treatments of two or more heavy particles (such as nucleons). It is desirable to regulate the contact interactions from the outset by treating them as having a finite range. The low energy physical observables should be insensitive to this range provided that the range is of a similar or greater scale than that of the interaction. Alternative schemes, such as dimensional regularization, lead to paradoxical conclusions such as the impossibility of repulsive interactions for truly low energy effective theories where all of the exchange particles are integrated out. This difficulty arises because a nonrelativistic field theory with repulsive contact interactions is trivial in the sense that the $S$ matrix is unity and the renormalized coupling constant zero. Possible consequences of low energy attraction are also discussed. It is argued that in the case of large or small scattering lengths, the region of validity of effective field theory expansion is much larger if the contact interactions are given a finite range from the beginning.Comment: 7 page

Directional interacting whispering gallery modes in coupled dielectric microdisks

We study the optical interaction in a coupled dielectric microdisks by investigating the splitting of resonance positions of interacting whispering gallery modes (WGMs) and their pattern change, depending on the distance between the microdisks. It is shown that the interaction between the WGMs with odd parity about y-axis becomes appreciable at a distance less than a wavelength and causes directional emissions of the resulting interacting WGMs. The directionality of the interacting WGMs can be understood in terms of an effective boundary deformation in ray dynamical analysis. We also discuss about the oscillation of the splitting when the distance is greater than a wavelength.Comment: 7 pages, 10 figure

Operational time and in-sample density forecasting

In this paper we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then re-use the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study

Nonparametric regression with parametric help

In this paper we propose a new nonparametric regression technique. Our proposal has common ground with existing two-step procedures in that it starts with a parametric model. However, our approach di↵ers from others in the choice of parametric start within the parametric family. Our proposal chooses a function that is the projection of the unknown regression function onto the parametric family in a certain metric, while the existing methods select the best approximation in the usual L2 metric. We find that the di↵erence leads to substantial improvement in the performance of regression estimators in comparison with direct one-step estimation, irrespective of the choice of a parametric model. This is in contrast with the existing two-step methods, which fail if the chosen parametric model is largely misspecified. We demonstrate this with sound theory and numerical experiment