CollocInfer: Collocation Inference in Differential Equation Models

This monograph details the implementation and use of the CollocInfer package in R for smoothing-based estimation of continuous-time nonlinear dynamic systems. These routines represent an extension of the generalized profiling methods in Ramsay, Hooker, Campbell, and Cao (2007) for estimating parameters in nonlinear ordinary differential equations. An interface to the fda package is included. The package also supports discretetime systems. We describe the methodological and computational framework and the necessary steps to use the software. Equivalent functionality is available in MATLAB

There\u27s a Pattern Here: The Case to Integrate Environmental Security into Homeland Security Strategy

The time is long overdue to acknowledge that global climate and resource stresses, encompassed by the concept of environmental security (ES), are an increasingly important part of homeland security (HS) study and practice, by even the most restricted definitions of HS. Environmental security issues will affect global economic and political stability, US national interests, and the risk of war and terrorism. Just as homeland security encompasses many complex issues and interconnected subfields, environmental security (ES) is interdisciplinary by nature. In essence, ES is an emergent discipline borrowing from a combination of environmental studies — which decades ago integrated environmental science with public policy — and the broader observations of how environmental change, extreme weather events and resource scarcity issues impact domestic and international security. In a two-part argument, we first observe the growing environmental and resource-related security threats at every level of analysis, from global to individual levels as consequences of warming-induced climate alterations. Next, given the significant impacts on local, regional, and international geopolitical stability, we discuss why environmental security threats must be incorporated into both homeland and national security strategic planning. Developing a theory of environmental security seems central to a more complete understanding of homeland security and a more modern concept of national security

Functional Data Analysis of Amplitude and Phase Variation

The abundance of functional observations in scientific endeavors has led to a significant development in tools for functional data analysis (FDA). This kind of data comes with several challenges: infinite-dimensionality of function spaces, observation noise, and so on. However, there is another interesting phenomena that creates problems in FDA. The functional data often comes with lateral displacements/deformations in curves, a phenomenon which is different from the height or amplitude variability and is termed phase variation. The presence of phase variability artificially often inflates data variance, blurs underlying data structures, and distorts principal components. While the separation and/or removal of phase from amplitude data is desirable, this is a difficult problem. In particular, a commonly used alignment procedure, based on minimizing the $\mathbb{L}^2$ norm between functions, does not provide satisfactory results. In this paper we motivate the importance of dealing with the phase variability and summarize several current ideas for separating phase and amplitude components. These approaches differ in the following: (1) the definition and mathematical representation of phase variability, (2) the objective functions that are used in functional data alignment, and (3) the algorithmic tools for solving estimation/optimization problems. We use simple examples to illustrate various approaches and to provide useful contrast between them.Comment: Published at http://dx.doi.org/10.1214/15-STS524 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province

We propose a method for the analysis of functional data with complex dependencies, such as spatially dependent curves or time dependent surfaces, over highly textured domains. The models are based on the idea of regression with partial differential regularizations. In particular, we consider here two roughness penalties that account separately for the regularity of the field in space and in time. Among the various modelling features, the proposed method is able to deal with spatial domains featuring peninsulas, islands and other complex geometries. Space-time varying covariate information is included in the model via a semi-parametric framework. The proposed method is compared via simulation studies to other spatiotemporal techniques and it is applied to the analysis of the annual production of waste in the towns of Venice province

Statistics of time warpings and phase variations

Many methods exist for one dimensional curve registration, and how methods compare has not been made clear in the literature. This special section is a summary of a detailed comparison of a number of major methods, done during a recent workshop. The basis of the comparison was simultaneous analysis of a set of four real data sets, which engendered a high level of informative discussion. Most research groups in this area were represented, and many insights were gained, which are discussed here. The format of this special section is four papers introducing the data, each accompanied by a number of analyses by different groups, plus a discussion summary of the lessons learned

Functional Data Analysis of Amplitude and Phase Variation

The abundance of functional observations in scientific endeavors has led to a significant development in tools for functional data analysis (FDA). This kind of data comes with several challenges: infinite-dimensionality of function spaces, observation noise, and so on. However, there is another interesting phenomena that creates problems in FDA. The functional data often comes with lateral displacements/deformations in curves, a phenomenon which is different from the height or amplitude variability and is terme

Adaptive estimation in circular functional linear models

We consider the problem of estimating the slope parameter in circular functional linear regression, where scalar responses Y1,...,Yn are modeled in dependence of 1-periodic, second order stationary random functions X1,...,Xn. We consider an orthogonal series estimator of the slope function, by replacing the first m theoretical coefficients of its development in the trigonometric basis by adequate estimators. Wepropose a model selection procedure for m in a set of admissible values, by defining a contrast function minimized by our estimator and a theoretical penalty function; this first step assumes the degree of ill posedness to be known. Then we generalize the procedure to a random set of admissible m's and a random penalty function. The resulting estimator is completely data driven and reaches automatically what is known to be the optimal minimax rate of convergence, in term of a general weighted L2-risk. This means that we provide adaptive estimators of both the slope function and its derivatives

Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs

Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to arbitrary accuracy any continuous mapping from a compact sub-space of a functional space to R. Moreover, we provide a consistency result that shows that any mapping from a functional space to R can be learned thanks to examples by a projection based MLP: the generalization mean square error of the MLP decreases to the smallest possible mean square error on the data when the number of examples goes to infinity

Report of the first international liver transplantation society expert panel consensus conference on renal insufficiency in liver transplantation

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64330/1/21877_ftp.pd

Vascular responses of the extremities to transdermal application of vasoactive agents in Caucasian and African descent individuals

This is an accepted manuscript of an article published by Springer in European Journal of Applied Physiology on 04/04/2015, available online: https://doi.org/10.1007/s00421-015-3164-2 The accepted version of the publication may differ from the final published version.© 2015, Springer-Verlag Berlin Heidelberg. Purpose: Individuals of African descent (AFD) are more susceptible to non-freezing cold injury than Caucasians (CAU) which may be due, in part, to differences in the control of skin blood flow. We investigated the skin blood flow responses to transdermal application of vasoactive agents. Methods: Twenty-four young males (12 CAU and 12 AFD) undertook three tests in which iontophoresis was used to apply acetylcholine (ACh 1 w/v %), sodium nitroprusside (SNP 0.01 w/v %) and noradrenaline (NA 0.5 mM) to the skin. The skin sites tested were: volar forearm, non-glabrous finger and toe, and glabrous finger (pad) and toe (pad). Results: In response to SNP on the forearm, AFD had less vasodilatation for a given current application than CAU (P = 0.027–0.004). ACh evoked less vasodilatation in AFD for a given application current in the non-glabrous finger and toe compared with CAU (P = 0.043–0.014) with a lower maximum vasodilatation in the non-glabrous finger (median [interquartile], AFD n = 11, 41[234] %, CAU n = 12, 351[451] %, P = 0.011) and non-glabrous toe (median [interquartile], AFD n = 9, 116[318] %, CAU n = 12, 484[720] %, P = 0.018). ACh and SNP did not elicit vasodilatation in the glabrous skin sites of either group. There were no ethnic differences in response to NA. Conclusion: AFD have an attenuated endothelium-dependent vasodilatation in non-glabrous sites of the fingers and toes compared with CAU. This may contribute to lower skin temperature following cold exposure and the increased risk of cold injuries experienced by AFD.Published versio