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Object Oriented Data Analysis of Cell-Well Structured Data
data objects cell con uence bright
2013/4/28
Object oriented data analysis (OODA) aims at statistically analyzing populations of complicated objects. This paper is motivated by a study of cell images in cell culture biology, which highlights a c...
On the sphericity test with large-dimensional observations
Large-dimensional data Sphericity test John’stest CLT for linear spectral statistics Large-dimensional sample covariance matrix
2013/4/27
In this paper, we propose corrections to LRT and John's test for sphericity in large-dimension. New formula for the limiting parameters in the CLT for linear spectral statistics of sample covariance m...
Regression with Distance Matrices
functional data analysis mixed data multidimensional scaling shape correlation ma-trix
2013/4/27
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these...
Topic Discovery through Data Dependent and Random Projections
Topic Discovery through Data Dependent and Random Projections
2013/4/27
We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a cond...
Variational Semi-blind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM
Variational Bayesian inference posterior image distribution image reconstruction hyperparameter estimation MRFM experiment
2013/5/2
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind d...
A multiple filter test for change point detection in renewal processes with varying variance
A multiple filter test change point detection renewal processes varying variance
2013/4/27
Non-stationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train record...
We define and study a generalization of Sobol sensitivity indices for the case of a vector output.
A dependent partition-valued process for multitask clustering and time evolving network modelling
A dependent partition-valued process multitask clustering time evolving network modelling
2013/4/27
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach wo...
Group-Sparse Model Selection: Hardness and Relaxations
Signal Approximation Structured Sparsity Interpretability Tractability Dynamic Programming Compressive Sensing
2013/5/2
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these model...
Linear system identification using stable spline kernels and PLQ penalties
linear system identification bias-variance trade off kernel-based regularization robust statistics interior point methods piecewise linear quadratic densities
2013/4/27
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model order selection step is...
Convergence rate of Markov chain methods for genomic motif discovery
Gibbs sampler DNA slow mixing spectral gap multimodal
2013/4/27
We analyze the convergence rate of a simplified version of a popular Gibbs sampling method used for statistical discovery of gene regulatory binding motifs in DNA sequences. This sampler satisfies a v...
Recovering Non-negative and Combined Sparse Representations
underdetermined linear system sparse representations non-negative constraints orthogonal matching pursuit unique sparse solution
2013/5/2
The non-negative solution to an underdetermined linear system can be uniquely recovered sometimes, even without imposing any additional sparsity constraints. In this paper, we derive conditions under ...
Automated Bayesian System Identification with NARX Models
Automated Bayesian System Identification NARX Models
2013/5/2
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is...
Gaussian Processes for Nonlinear Signal Processing
Gaussian Processes Nonlinear Signal Processing
2013/5/2
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tut...
Machine Learning for Bioclimatic Modelling
Machine Learning Bioclimatic Modelling Geographic Range Artificial Neural Network Evolutionary Algorithm
2013/5/2
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range s...