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Efficient Utility-based Clustering over High Dimensional Partition Spaces
Efficient Utility-based Clustering High Dimensional Partition Spaces
2009/9/24
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest sco...
A Stochastic Partitioning Method to Associate High-dimensional Responses and Covariates
multivariate model selection mixture model Markov chain Mont Carlo parallel tempering CGH analysis
2009/9/24
We consider the problem of variable selection in data sets with many response variables and many covariates. A method is proposed that allows some covariates to affect some response var...
An asymptotic viewpoint on high-dimensional Bayesian testing
Bayesian testing high-dimensional testing rates of testing functional data analysis goodness-of-t testing
2009/9/22
The Bayesian point-null testing problem is studied asymptotically
under a high-dimensional normal-means model. A noninformative prior structure
is proposed for general problems, and then rened for t...
Smoothing ℓ₁-penalized estimators for high-dimensional time-course data
Lasso Local least squares Multivariate regression Variable selection Weighted likelihood
2009/9/16
When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use ℓ₁-penalized estimato...
Selection of variables and dimension reduction in high-dimensional non-parametric regression
dimension reduction high dimension LASSO
2009/9/16
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...
This paper explores the following question: what kind of statistical
guarantees can be given when doing variable selection in highdimensional
models? In particular, we look at the error rates and
p...
High-dimensional Gaussian model selection on a Gaussian design
High-dimensional Gaussian model selection Gaussian design
2010/4/30
High-dimensional Gaussian model selection on a Gaussian design。
SCAD-penalized regression in high-dimensional partially linear models
Asymptotic normality high-dimensional data oracle property penalized estimation semiparametric models variable selection
2010/3/19
We consider the problem of simultaneous variable selection and
estimation in partially linear models with a divergent number of covariates
in the linear part, under the assumption that the vector of...
Asymptotic Distribution of Coordinates on High Dimensional Spheres
Asymptotic Distribution Coordinates Dimensional Sphere
2009/3/27
The coordinates xi of a point x = (x1, x2,..., xn) chosen at random according to a uniform distribution on the l2(n)-sphere of radius n1/2 have approximately a normal distribution when n is large. The...
Asymptotic Distribution of Coordinates on High Dimensional Spheres
Asymptotic Distribution Coordinates Dimensional Spheres
2009/3/23
The coordinates xi of a point x = (x1, x2,..., xn) chosen at random according to a uniform distribution on the l2(n)-sphere of radius n1/2 have approximately a normal distribution when n is large. The...
Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
Adaptive Lasso High Dimensional Regression Gaussian Graphical Modeling
2010/3/18
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional
model selection in linear and Gaussian graphical models. Our conditions for consistency cover more
...
Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
Hiroshima University
2009/3/9
This paper is concerned with Dempster trace criterion for multivariate linear hypothesis which was proposed for high dimensional situation. First we derive asymptotic null and nonnull distributions of...
Some Tests Concerning the Covariance Matrix in High Dimensional Data
asymptotic distributions multivariate normal null and non-null distributions sample size smaller than the dimension
2009/3/9
In this paper, tests are developed for testing certain hypotheses on the covariance matrix Σ, when the sample size N = n + 1 is smaller than the dimension pof the data. Under the condition that (tr Σi...
Comparison of Discrimination Methods for High Dimensional Data
classification discrimination analysis minimum distance Moore-Penrose inverse
2009/3/5
In microarray experiments, the dimension p of the data is very large but there are only a few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of...
Multivariate Theory for Analyzing High Dimensional Data
distribution of test statistics DNA microarray data fewer observations than dimension multivariate analysis of variance singular Wishart
2009/3/5
In this article, we develop a multivariate theory for analyzing multivariate datasets that have fewer observations than dimensions. More specifically, we consider the problem of testing the hypothesis...