15,654 research outputs found
Deconvolution, differentiation and Fourier transformation algorithms for noise-containing data based on splines and global approximation
One of the main problems in the analysis of measured spectra is how to reduce the influence of noise in data processing. We show a deconvolution, a differentiation and a Fourier Transform algorithm that can be run on a small computer (64 K RAM) and suffer less from noise than commonly used routines. This objective is achieved by implementing spline based functions in mathematical operations to obtain global approximation properties in our routines. The convenient behaviour and the pleasant mathematical character of splines makes it possible to perform these mathematical operations on large data input in a limited computing time on a small computer system. Comparison is made with widely used routines
Fast Selection of Spectral Variables with B-Spline Compression
The large number of spectral variables in most data sets encountered in
spectral chemometrics often renders the prediction of a dependent variable
uneasy. The number of variables hopefully can be reduced, by using either
projection techniques or selection methods; the latter allow for the
interpretation of the selected variables. Since the optimal approach of testing
all possible subsets of variables with the prediction model is intractable, an
incremental selection approach using a nonparametric statistics is a good
option, as it avoids the computationally intensive use of the model itself. It
has two drawbacks however: the number of groups of variables to test is still
huge, and colinearities can make the results unstable. To overcome these
limitations, this paper presents a method to select groups of spectral
variables. It consists in a forward-backward procedure applied to the
coefficients of a B-Spline representation of the spectra. The criterion used in
the forward-backward procedure is the mutual information, allowing to find
nonlinear dependencies between variables, on the contrary of the generally used
correlation. The spline representation is used to get interpretability of the
results, as groups of consecutive spectral variables will be selected. The
experiments conducted on NIR spectra from fescue grass and diesel fuels show
that the method provides clearly identified groups of selected variables,
making interpretation easy, while keeping a low computational load. The
prediction performances obtained using the selected coefficients are higher
than those obtained by the same method applied directly to the original
variables and similar to those obtained using traditional models, although
using significantly less spectral variables
RAMPAC: a program for analysis of complicated Raman spectra
A computer program for the analysis of complicated (e.g. multi-line) Raman spectra is described. The program includes automatic peak search, various procedures for background determination, peak fit and spectrum deconvolution and extensive spectrum handling procedures
Trajectory Reconstruction Techniques for Evaluation of ATC Systems
This paper is focused on trajectory reconstruction techniques for evaluating ATC systems, using real data of recorded opportunity traffic. We analyze different alternatives for this problem, from traditional interpolation approaches based on curve fitting to our proposed schemes based on modeling regular motion patterns with optimal smoothers. The extraction of trajectory features such as motion type (or mode of flight), maneuvers profile, geometric parameters, etc., allows a more accurate computation of the curve and the detailed evaluation of the data processors used in the ATC centre. Different alternatives will be compared with some performance results obtained with simulated and real data sets
A universal approximate cross-validation criterion and its asymptotic distribution
A general framework is that the estimators of a distribution are obtained by
minimizing a function (the estimating function) and they are assessed through
another function (the assessment function). The estimating and assessment
functions generally estimate risks. A classical case is that both functions
estimate an information risk (specifically cross entropy); in that case Akaike
information criterion (AIC) is relevant. In more general cases, the assessment
risk can be estimated by leave-one-out crossvalidation. Since leave-one-out
crossvalidation is computationally very demanding, an approximation formula can
be very useful. A universal approximate crossvalidation criterion (UACV) for
the leave-one-out crossvalidation is given. This criterion can be adapted to
different types of estimators, including penalized likelihood and maximum a
posteriori estimators, and of assessment risk functions, including information
risk functions and continuous rank probability score (CRPS). This formula
reduces to Takeuchi information criterion (TIC) when cross entropy is the risk
for both estimation and assessment. The asymptotic distribution of UACV and of
a difference of UACV is given. UACV can be used for comparing estimators of the
distributions of ordered categorical data derived from threshold models and
models based on continuous approximations. A simulation study and an analysis
of real psychometric data are presented.Comment: 23 pages, 2 figure
Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification
We consider the high energy physics unfolding problem where the goal is to
estimate the spectrum of elementary particles given observations distorted by
the limited resolution of a particle detector. This important statistical
inverse problem arising in data analysis at the Large Hadron Collider at CERN
consists in estimating the intensity function of an indirectly observed Poisson
point process. Unfolding typically proceeds in two steps: one first produces a
regularized point estimate of the unknown intensity and then uses the
variability of this estimator to form frequentist confidence intervals that
quantify the uncertainty of the solution. In this paper, we propose forming the
point estimate using empirical Bayes estimation which enables a data-driven
choice of the regularization strength through marginal maximum likelihood
estimation. Observing that neither Bayesian credible intervals nor standard
bootstrap confidence intervals succeed in achieving good frequentist coverage
in this problem due to the inherent bias of the regularized point estimate, we
introduce an iteratively bias-corrected bootstrap technique for constructing
improved confidence intervals. We show using simulations that this enables us
to achieve nearly nominal frequentist coverage with only a modest increase in
interval length. The proposed methodology is applied to unfolding the boson
invariant mass spectrum as measured in the CMS experiment at the Large Hadron
Collider.Comment: Published at http://dx.doi.org/10.1214/15-AOAS857 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org). arXiv admin note:
substantial text overlap with arXiv:1401.827
Approximating Data with weighted smoothing Splines
Given a data set (t_i, y_i), i=1,..., n with the t_i in [0,1] non-parametric
regression is concerned with the problem of specifying a suitable function
f_n:[0,1] -> R such that the data can be reasonably approximated by the points
(t_i, f_n(t_i)), i=1,..., n. If a data set exhibits large variations in local
behaviour, for example large peaks as in spectroscopy data, then the method
must be able to adapt to the local changes in smoothness. Whilst many methods
are able to accomplish this they are less successful at adapting derivatives.
In this paper we show how the goal of local adaptivity of the function and its
first and second derivatives can be attained in a simple manner using weighted
smoothing splines. A residual based concept of approximation is used which
forces local adaptivity of the regression function together with a global
regularization which makes the function as smooth as possible subject to the
approximation constraints
A sparse-grid isogeometric solver
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS
as a basis for the approximation of the solution of PDEs. In this work, we
investigate to which extent IGA solvers can benefit from the so-called
sparse-grids construction in its combination technique form, which was first
introduced in the early 90s in the context of the approximation of
high-dimensional PDEs. The tests that we report show that, in accordance to the
literature, a sparse-grid construction can indeed be useful if the solution of
the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the
case of non-smooth solutions when some a-priori knowledge on the location of
the singularities of the solution can be exploited to devise suitable
non-equispaced meshes. Finally, we remark that sparse grids can be seen as a
simple way to parallelize pre-existing serial IGA solvers in a straightforward
fashion, which can be beneficial in many practical situations.Comment: updated version after revie
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