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Functional and Parametric Estimation in a Semi- and Nonparametric Model with Application to Mass-Spectrometry Data
Local linear regression Bandwidth selection Nonparamet-ric estimation
2013/6/13
Motivated by modeling and analysis of mass-spectrometry data, a semi- and nonparametric model is proposed that consists of a linear parametric component for individual location and scale and a nonpara...
On the Convergence and Consistency of the Blurring Mean-Shift Process
Mean-shift Convergence Consistency Clustering,γ-divergence Super robustness
2013/6/13
The mean-shift algorithm is a popular algorithm in computer vision and image processing. It can also be cast as a minimum gamma-divergence estimation. In this paper we focus on the "blurring" mean shi...
Moment based estimation of supOU processes and a related stochastic volatility model
generalized method of moments Ornstein-Uhlenbeck type process L
2013/6/14
After a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU SV model we estimate these processes by using the generalized method of moments. We show that t...
Counting processes for correlated binary responses
Continuous-time Markov process dependent Bernoulli trials developmental toxicity famil-ial disease clustering probabilistic embedding teratology
2013/6/14
We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of dependent Bernoulli random variable...
Statistical Analysis of Metric Graph Reconstruction
Metric Graph Filament Reconstruction Manifold Learning Minimax Esti-mation
2013/6/13
A metric graph is a 1-dimensional stratified metric space consisting of vertices and edges or loops glued together. Metric graphs can be naturally used to represent and model data that take the form o...
Calculation of Exact Estimators by Integration Over the Surface of an n-Dimensional Sphere
Calculation Exact Estimators Integration Over the Surface an n-Dimensional Sphere
2013/6/13
This paper reconsiders the problem of calculating the expected set of probabilities , given the observed set of items {m_i}, that are distributed among n bins with an (unknown) set of probabiliti...
Relative Performance of Expected and Observed Fisher Information in Covariance Estimation for Maximum Likelihood Estimates
Relative Performance Expected and Observed Fisher Information Covariance Estimation Maximum Likelihood Estimates
2013/6/13
Maximum likelihood estimation is a popular method in statistical inference. As a way of assessing the accuracy of the maximum likelihood estimate (MLE), the calculation of the covariance matrix of the...
A Mixture of Generalized Hyperbolic Distributions
Mixture Generalized Hyperbolic Distributions
2013/6/13
We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives of which the mixture of multivariat...
Automatic Estimation of Flux Distributions of Astrophysical Source Populations
Broken power law CDFN X-ray survey Interwoven EM algorithm Likelihood com-putations logN logS Pareto distribution
2013/6/13
In astrophysics a common goal is to infer the flux distribution of populations of scientifically interesting objects such as pulsars or supernovae. In practice, inference for the flux distribution is ...
An Improved EM algorithm
Sensitivity analysis Convergence analysis Expectation Maximization K-means K-medoids
2013/6/14
In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we g...
Anisotropic oracle inequalities in noisy quantization
Quantization Deconvolution Fast rates Margin assumption,k-means clus-tering
2013/6/13
The effect of errors in variables in quantization is investigated. We prove general exact and non-exact oracle inequalities with fast rates for an empirical minimization based on a noisy sample $Z_i=X...
Tensor Decompositions: A New Concept in Brain Data Analysis?
Multilinear BSS linked multiway BSS/ICA tensor factorizations and de-compositions constrained Tucker and CP models PenalizedTensor Decompositions (PTD) feature extraction classification multiway PLS and CCA
2013/6/14
Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway In...
The problem of estimating the number of unique types or distinct species in a group occurs in many fields, but is not a straightforward one. Given an arbitrary probabilistic distribution of entries to...
Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee
Mixtures of Bernoulli Templates Two-Round EM Performance Guarantee
2013/6/13
Dasgupta showed that a two-round variant of the EM algorithm can learn mixture of Gaussian distributions with near optimal precision with high probability if the Gaussian distributions are well separa...
Guaranteed Sparse Recovery under Linear Transformation
Guaranteed Sparse Recovery Linear Transformation
2013/6/13
We consider the following signal recovery problem: given a measurement matrix $\Phi\in \mathbb{R}^{n\times p}$ and a noisy observation vector $c\in \mathbb{R}^{n}$ constructed from $c = \Phi\theta^* +...