2,172 research outputs found
Deconvolution Estimation in Measurement Error Models: The R Package decon
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples.
Deconvolution Estimation in Measurement Error Models: The R Package decon
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples
The extinction law from photometric data: linear regression methods
Context. The properties of dust grains, in particular their size
distribution, are expected to differ from the interstellar medium to the
high-density regions within molecular clouds. Since the extinction at
near-infrared wavelengths is caused by dust, the extinction law in cores should
depart from that found in low-density environments if the dust grains have
different properties. Aims. We explore methods to measure the near-infrared
extinction law produced by dense material in molecular cloud cores from
photometric data. Methods. Using controlled sets of synthetic and
semi-synthetic data, we test several methods for linear regression applied to
the specific problem of deriving the extinction law from photometric data. We
cover the parameter space appropriate to this type of observations. Results. We
find that many of the common linear-regression methods produce biased results
when applied to the extinction law from photometric colors. We propose and
validate a new method, LinES, as the most reliable for this effect. We explore
the use of this method to detect whether or not the extinction law of a given
reddened population has a break at some value of extinction.Comment: 15 pages, 18 figures, accepted to A&A, in pres
Measurement error caused by spatial misalignment in environmental epidemiology
Copyright @ 2009 Gryparis et al - Published by Oxford University Press.In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.This research was supported by NIEHS grants ES012044 (AG, BAC), ES009825 (JS, BAC), ES007142 (CJP), and ES000002 (CJP), and EPA grant R-832416 (JS, BAC)
Bayesian Measurement Error Correction in Structured Additive Distributional Regression with an Application to the Analysis of Sensor Data on Soil-Plant Variability
The flexibility of the Bayesian approach to account for covariates with
measurement error is combined with semiparametric regression models for a class
of continuous, discrete and mixed univariate response distributions with
potentially all parameters depending on a structured additive predictor. Markov
chain Monte Carlo enables a modular and numerically efficient implementation of
Bayesian measurement error correction based on the imputation of unobserved
error-free covariate values. We allow for very general measurement errors,
including correlated replicates with heterogeneous variances. The proposal is
first assessed by a simulation trial, then it is applied to the assessment of a
soil-plant relationship crucial for implementing efficient agricultural
management practices. Observations on multi-depth soil information forage
ground-cover for a seven hectares Alfalfa stand in South Italy were obtained
using sensors with very refined spatial resolution. Estimating a functional
relation between ground-cover and soil with these data involves addressing
issues linked to the spatial and temporal misalignment and the large data size.
We propose a preliminary spatial interpolation on a lattice covering the field
and subsequent analysis by a structured additive distributional regression
model accounting for measurement error in the soil covariate. Results are
interpreted and commented in connection to possible Alfalfa management
strategies
Bayesian Semiparametric Multivariate Density Deconvolution
We consider the problem of multivariate density deconvolution when the
interest lies in estimating the distribution of a vector-valued random variable
but precise measurements of the variable of interest are not available,
observations being contaminated with additive measurement errors. The existing
sparse literature on the problem assumes the density of the measurement errors
to be completely known. We propose robust Bayesian semiparametric multivariate
deconvolution approaches when the measurement error density is not known but
replicated proxies are available for each unobserved value of the random
vector. Additionally, we allow the variability of the measurement errors to
depend on the associated unobserved value of the vector of interest through
unknown relationships which also automatically includes the case of
multivariate multiplicative measurement errors. Basic properties of finite
mixture models, multivariate normal kernels and exchangeable priors are
exploited in many novel ways to meet the modeling and computational challenges.
Theoretical results that show the flexibility of the proposed methods are
provided. We illustrate the efficiency of the proposed methods in recovering
the true density of interest through simulation experiments. The methodology is
applied to estimate the joint consumption pattern of different dietary
components from contaminated 24 hour recalls
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