1,850 research outputs found
R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses
The R package multgee implements the local odds ratios generalized estimating
equations (GEE) approach proposed by Touloumis et al. (2013), a GEE approach
for correlated multinomial responses that circumvents theoretical and practical
limitations of the GEE method. A main strength of multgee is that it provides
GEE routines for both ordinal (ordLORgee) and nominal (nomLORgee) responses,
while relevant softwares in R and SAS are restricted to ordinal responses under
a marginal cumulative link model specification. In addition, multgee offers a
marginal adjacent categories logit model for ordinal responses and a marginal
baseline category logit model for nominal. Further, utility functions are
available to ease the local odds ratios structure selection (intrinsic.pars)
and to perform a Wald type goodness-of-fit test between two nested GEE models
(waldts). We demonstrate the application of multgee through a clinical trial
with clustered ordinal multinomial responses.Comment: To appear in Journal of Statistical Softwar
Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings
Estimating a covariance matrix is an important task in applications where the
number of variables is larger than the number of observations. Shrinkage
approaches for estimating a high-dimensional covariance matrix are often
employed to circumvent the limitations of the sample covariance matrix. A new
family of nonparametric Stein-type shrinkage covariance estimators is proposed
whose members are written as a convex linear combination of the sample
covariance matrix and of a predefined invertible target matrix. Under the
Frobenius norm criterion, the optimal shrinkage intensity that defines the best
convex linear combination depends on the unobserved covariance matrix and it
must be estimated from the data. A simple but effective estimation process that
produces nonparametric and consistent estimators of the optimal shrinkage
intensity for three popular target matrices is introduced. In simulations, the
proposed Stein-type shrinkage covariance matrix estimator based on a scaled
identity matrix appeared to be up to 80% more efficient than existing ones in
extreme high-dimensional settings. A colon cancer dataset was analyzed to
demonstrate the utility of the proposed estimators. A rule of thumb for adhoc
selection among the three commonly used target matrices is recommended.Comment: To appear in Computational Statistics and Data Analysi
Poisson inverse problems
In this paper we focus on nonparametric estimators in inverse problems for
Poisson processes involving the use of wavelet decompositions. Adopting an
adaptive wavelet Galerkin discretization, we find that our method combines the
well-known theoretical advantages of wavelet--vaguelette decompositions for
inverse problems in terms of optimally adapting to the unknown smoothness of
the solution, together with the remarkably simple closed-form expressions of
Galerkin inversion methods. Adapting the results of Barron and Sheu [Ann.
Statist. 19 (1991) 1347--1369] to the context of log-intensity functions
approximated by wavelet series with the use of the Kullback--Leibler distance
between two point processes, we also present an asymptotic analysis of
convergence rates that justifies our approach. In order to shed some light on
the theoretical results obtained and to examine the accuracy of our estimates
in finite samples, we illustrate our method by the analysis of some simulated
examples.Comment: Published at http://dx.doi.org/10.1214/009053606000000687 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Near-Infrared Imaging of White Dwarfs with Candidate Debris Disks
We have carried out imaging of 12 white dwarf debris disk candidates
from the WIRED SDSS DR7 catalog, aiming to confirm or rule out disks among
these sources. On the basis of positional identification and the flux density
spectra, we find that seven white dwarfs have excess infrared emission, but
mostly at WISE W1 and W2 bands, four are due to nearby red objects consistent
with background galaxies or very low mass dwarfs, and one exhibits excess
emission at consistent with an unresolved L0 companion at the correct
distance. While our photometry is not inconsistent with all seven excesses
arising from disks, the stellar properties are distinct from the known
population of debris disk white dwarfs, making the possibility questionable. In
order to further investigate the nature of these infrared sources, warm Spitzer
imaging is needed, which may help resolve galaxies from the white dwarfs and
provide more accurate flux measurements.Comment: 9 pages, 3 figures, significant comments from Referee were
incorporated, accepted for publication in Ap
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study
Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced
From library skills to information literacy
The application of new technologies and the acquisition of new sources and methods of information dissemination, as well as the provision of libraries services, requires the special education of the users in order to take advantage of these sources and services. In this paper, an investigation of the Greek academic libraries and their user education sessions is attempted. This research aims to explore the user education sessions offered by the libraries, with special regards to the education, the type of user education sessions and their contents. For the collection of the elements, the questionnaire method is selected. The current situation as much as it concerns the libraries and the applied teaching methods at the Greek education institutions, is presented
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