2,461 research outputs found
Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates
We propose generalized additive partial linear models for complex data which
allow one to capture nonlinear patterns of some covariates, in the presence of
linear components. The proposed method improves estimation efficiency and
increases statistical power for correlated data through incorporating the
correlation information. A unique feature of the proposed method is its
capability of handling model selection in cases where it is difficult to
specify the likelihood function. We derive the quadratic inference
function-based estimators for the linear coefficients and the nonparametric
functions when the dimension of covariates diverges, and establish asymptotic
normality for the linear coefficient estimators and the rates of convergence
for the nonparametric functions estimators for both finite and high-dimensional
cases. The proposed method and theoretical development are quite challenging
since the numbers of linear covariates and nonlinear components both increase
as the sample size increases. We also propose a doubly penalized procedure for
variable selection which can simultaneously identify nonzero linear and
nonparametric components, and which has an asymptotic oracle property.
Extensive Monte Carlo studies have been conducted and show that the proposed
procedure works effectively even with moderate sample sizes. A pharmacokinetics
study on renal cancer data is illustrated using the proposed method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1194 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Robust approach for variable selection with high dimensional Logitudinal data analysis
This paper proposes a new robust smooth-threshold estimating equation to
select important variables and automatically estimate parameters for high
dimensional longitudinal data. A novel working correlation matrix is proposed
to capture correlations within the same subject. The proposed procedure works
well when the number of covariates p increases as the number of subjects n
increases. The proposed estimates are competitive with the estimates obtained
with the true correlation structure, especially when the data are contaminated.
Moreover, the proposed method is robust against outliers in the response
variables and/or covariates. Furthermore, the oracle properties for robust
smooth-threshold estimating equations under "large n, diverging p" are
established under some regularity conditions. Extensive simulation studies and
a yeast cell cycle data are used to evaluate the performance of the proposed
method, and results show that our proposed method is competitive with existing
robust variable selection procedures.Comment: 32 pages, 7 tables, 5 figure
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
Statistical Modelling
The book collects the proceedings of the 19th International Workshop on Statistical Modelling held in Florence on July 2004. Statistical modelling is an important cornerstone in many scientific disciplines, and the workshop has provided a rich environment for cross-fertilization of ideas from different disciplines. It consists in four invited lectures, 48 contributed papers and 47 posters. The contributions are arranged in sessions: Statistical Modelling; Statistical Modelling in Genomics; Semi-parametric Regression Models; Generalized Linear Mixed Models; Correlated Data Modelling; Missing Data, Measurement of Error and Survival Analysis; Spatial Data Modelling and Time Series and Econometrics
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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