4,679 research outputs found

    Optimal Defaults and Active Decisions

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    Defaults can have a dramatic influence on consumer decisions. We identify an overlooked but practical alternative to defaults: requiring individuals to make an explicit choice for themselves. We study such "active decisions" in the context of 401(k) saving. We find that compelling new hires to make active decisions about 401(k) enrollment raises the initial fraction that enroll by 28 percentage points relative to a standard opt-in enrollment procedure, producing a savings distribution three months after hire that would take three years to achieve under standard enrollment. We also present a model of 401(k) enrollment and characterize the optimal enrollment regime. Active decisions are optimal when consumers have a strong propensity to procrastinate and savings preferences that are highly heterogeneous. Naive beliefs about future time-inconsistency strengthen the normative appeal of the active-decision enrollment regime. However, financial illiteracy favors default enrollment over active decision enrollment.

    Spatially Adaptive Bayesian P-Splines with Heteroscedastic Errors

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    An increasingly popular tool for nonparametric smoothing are penalized splines (P-splines) which use low-rank spline bases to make computations tractable while maintaining accuracy as good as smoothing splines. This paper extends penalized spline methodology by both modeling the variance function nonparametrically and using a spatially adaptive smoothing parameter. These extensions have been studied before, but never together and never in the multivariate case. This combination is needed for satisfactory inference and can be implemented effectively by Bayesian \mbox{MCMC}. The variance process controlling the spatially-adaptive shrinkage of the mean and the variance of the heteroscedastic error process are modeled as log-penalized splines. We discuss the choice of priors and extensions of the methodology,in particular, to multivariate smoothing using low-rank thin plate splines. A fully Bayesian approach provides the joint posterior distribution of all parameters, in particular, of the error standard deviation and penalty functions. In the multivariate case we produce maps of the standard deviation and penalty functions. Our methodology can be implemented using the Bayesian software WinBUGS

    Boundary fields and renormalization group flow in the two-matrix model

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    We analyze the Ising model on a random surface with a boundary magnetic field using matrix model techniques. We are able to exactly calculate the disk amplitude, boundary magnetization and bulk magnetization in the presence of a boundary field. The results of these calculations can be interpreted in terms of renormalization group flow induced by the boundary operator. In the continuum limit this RG flow corresponds to the flow from non-conformal to conformal boundary conditions which has recently been studied in flat space theories.Comment: 31 pages, Late

    The application of a long period grating sensors to human respiratory plethysmography

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    A series of nine in-line curvature sensors on a garment are used to monitor the thoracic and abdominal movements of a human during respiration for application to Human Respiratory Plethysmography. These results are used to obtain volumetric tidal changes of the human torso which show agreement with data from a spirometer used simultaneously to recorded the inspired and expired volume at the mouth during both rhythmic and transient breathing patterns. The curvature sensors are based upon long period gratings which are written in a progressive three layered fibre to render them insensitive to refractive index changes. The sensor consists of the long period grating laid upon a carbon fibre ribbon, with this then encapsulated in a low temperature curing silicone rubber. The sensing array is multiplexed and interrogated using a derivative spectroscopy based technique to monitor the response of the LPGs' attenuation bands to curvature. The versatility of this scheme is demonstrated by applying the same garment and sensors to various human body types and sizes. It was also found from statistical analysis of the sensing array data, in conjunction with the measurements taken with a spirometer, that 11 to 12 sensors should be required to obtain an absolute volumetric error of 5%

    Minimal Dynamical Triangulations of Random Surfaces

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    We introduce and investigate numerically a minimal class of dynamical triangulations of two-dimensional gravity on the sphere in which only vertices of order five, six or seven are permitted. We show firstly that this restriction of the local coordination number, or equivalently intrinsic scalar curvature, leaves intact the fractal structure characteristic of generic dynamically triangulated random surfaces. Furthermore the Ising model coupled to minimal two-dimensional gravity still possesses a continuous phase transition. The critical exponents of this transition correspond to the usual KPZ exponents associated with coupling a central charge c=1/2 model to two-dimensional gravity.Comment: Latex, 9 pages, 3 figures, Published versio

    Redox-Based Probes for Protein Tyrosine Phosphatases

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    No AbstractPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83769/1/4423_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/83769/2/anie_201007871_sm_miscellaneous_information.pd

    Power Transformations When Fitting Theoretical Models to Data

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    We investigate power transformations in non-linear regression problems when there is a physical model for the response but little understanding of the underlying error structure. In such circumstances and unlike the ordinary power transformation model, both the response and the model must be transformed simultaneously and in the same way. We show by an asymptotic theory and a small Monte-Carlo study that for estimating the model parameters there is little cost for not knowing the correct transform a priori; this is in dramatic contrast to the results for the usual case that only the response is transformed
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