49,221 research outputs found
Robust Modeling Using Non-Elliptically Contoured Multivariate t Distributions
Models based on multivariate t distributions are widely applied to analyze
data with heavy tails. However, all the marginal distributions of the
multivariate t distributions are restricted to have the same degrees of
freedom, making these models unable to describe different marginal
heavy-tailedness. We generalize the traditional multivariate t distributions to
non-elliptically contoured multivariate t distributions, allowing for different
marginal degrees of freedom. We apply the non-elliptically contoured
multivariate t distributions to three widely-used models: the Heckman selection
model with different degrees of freedom for selection and outcome equations,
the multivariate Robit model with different degrees of freedom for marginal
responses, and the linear mixed-effects model with different degrees of freedom
for random effects and within-subject errors. Based on the Normal mixture
representation of our t distribution, we propose efficient Bayesian inferential
procedures for the model parameters based on data augmentation and parameter
expansion. We show via simulation studies and real examples that the
conclusions are sensitive to the existence of different marginal
heavy-tailedness
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
While linear mixed model (LMM) has shown a competitive performance in
correcting spurious associations raised by population stratification, family
structures, and cryptic relatedness, more challenges are still to be addressed
regarding the complex structure of genotypic and phenotypic data. For example,
geneticists have discovered that some clusters of phenotypes are more
co-expressed than others. Hence, a joint analysis that can utilize such
relatedness information in a heterogeneous data set is crucial for genetic
modeling.
We proposed the sparse graph-structured linear mixed model (sGLMM) that can
incorporate the relatedness information from traits in a dataset with
confounding correction. Our method is capable of uncovering the genetic
associations of a large number of phenotypes together while considering the
relatedness of these phenotypes. Through extensive simulation experiments, we
show that the proposed model outperforms other existing approaches and can
model correlation from both population structure and shared signals. Further,
we validate the effectiveness of sGLMM in the real-world genomic dataset on two
different species from plants and humans. In Arabidopsis thaliana data, sGLMM
behaves better than all other baseline models for 63.4% traits. We also discuss
the potential causal genetic variation of Human Alzheimer's disease discovered
by our model and justify some of the most important genetic loci.Comment: Code available at https://github.com/YeWenting/sGLM
Virtual noiseless amplification and Gaussian post-selection in continuous-variable quantum key distribution
The noiseless amplification or attenuation are two heralded filtering
operations that enable respectively to increase or decrease the mean field of
any quantum state of light with no added noise, at the cost of a small success
probability. We show that inserting such noiseless operations in a transmission
line improves the performance of continuous-variable quantum key distribution
over this line. Remarkably, these noiseless operations do not need to be
physically implemented but can simply be simulated in the data post-processing
stage. Hence, virtual noiseless amplification or attenuation amounts to perform
a Gaussian post-selection, which enhances the secure range or tolerable excess
noise while keeping the benefits of Gaussian security proofs.Comment: 8 pages, 5 figure
Continuous variable entanglement distillation of Non-Gaussian Mixed States
Many different quantum information communication protocols such as
teleportation, dense coding and entanglement based quantum key distribution are
based on the faithful transmission of entanglement between distant location in
an optical network. The distribution of entanglement in such a network is
however hampered by loss and noise that is inherent in all practical quantum
channels. Thus, to enable faithful transmission one must resort to the protocol
of entanglement distillation. In this paper we present a detailed theoretical
analysis and an experimental realization of continuous variable entanglement
distillation in a channel that is inflicted by different kinds of non-Gaussian
noise. The continuous variable entangled states are generated by exploiting the
third order non-linearity in optical fibers, and the states are sent through a
free-space laboratory channel in which the losses are altered to simulate a
free-space atmospheric channel with varying losses. We use linear optical
components, homodyne measurements and classical communication to distill the
entanglement, and we find that by using this method the entanglement can be
probabilistically increased for some specific non-Gaussian noise channels
Fixed effects selection in the linear mixed-effects model using adaptive ridge procedure for L0 penalty performance
This paper is concerned with the selection of fixed effects along with the
estimation of fixed effects, random effects and variance components in the
linear mixed-effects model. We introduce a selection procedure based on an
adaptive ridge (AR) penalty of the profiled likelihood, where the covariance
matrix of the random effects is Cholesky factorized. This selection procedure
is intended to both low and high-dimensional settings where the number of fixed
effects is allowed to grow exponentially with the total sample size, yielding
technical difficulties due to the non-convex optimization problem induced by L0
penalties. Through extensive simulation studies, the procedure is compared to
the LASSO selection and appears to enjoy the model selection consistency as
well as the estimation consistency
Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting
With the emergence of semi- and nonparametric regression the
generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed models. In contrast to common procedures it can be used in high-dimensional settings where many covariates are available and the form of the influence is unknown. It is constructed as a componentwise boosting method and hence is able to perform variable selection. The complexity of the resulting estimator is determined by information criteria. The method is nvestigated in simulation studies for binary and Poisson responses and is illustrated by using real data sets
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