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Some covariance models based on normal scale mixtures
cross covariance function Gneiting's class rainfall model spatio-temporal model
2011/3/24
Modelling spatio-temporal processes has become an important issue in current research. Since Gaussian processes are essentially determined by their second order structure, broad classes of covariance ...
Asymptotic properties of maximum likelihood estimators in models with multiple change points
change-point fraction common parameter consistency convergence rate Kullback–Leibler distance within-segment parameter
2011/3/24
Models with multiple change points are used in many fields; however, the theoretical properties of maximum likelihood estimators of such models have received relatively little attention. The goal of t...
Varying-coefficient functional linear regression
asymptotics eigenfunctions functional data analysis local polynomial smoothing longitudinal data varying-coeffi cient models
2011/3/24
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or m...
Functional linear regression via canonical analysis
canonical components covariance operator functional data analysis functional linear model longitudinal data parameter function stochastic process
2011/3/24
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresp...
Estimating the scaling function of multifractal measures and multifractal random walks using ratios
namely mutiplicative cascades structure function
2011/3/24
In this paper we prove central limit theorems for bias reduced estimators of the structure function of several multifractal processes, namely mutiplicative cascades, multifractal random measures, mult...
Selecting the rank of SVD by Maximum Approximation Capacity
Approximation Capacity Selecting the rank of SVD
2011/3/25
Truncated Singular Value Decomposition (SVD) calculates the closest rank-k approximation of a given input matrix. Selecting the appropriate rank k defines a critical model order choice in most applica...
Randomized algorithms for statistical image analysis and site percolation on square lattices
Image analysis signal detection percolation image reconstruction noisy image
2011/3/24
We propose a novel probabilistic method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algorithm that allows to detect obje...
Computationally efficient algorithms for statistical image processing. Implementation in R
Image analysis signal detection image recon-struction percolation noisy image unsupervised machine learning spatial statistics
2011/3/24
In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory ...
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Noisy matrix decomposition via convex relaxation high dimensions
2011/3/24
We analyze a class of estimators based on convex relaxation for solving high-dimensional matrix decomposition problems. The observations are the noisy realizations of the sum of an (appproximately) lo...
Detection of objects in noisy images and site percolation on square lattices
Image analysis signal detection image recon-struction percolation noisy image
2011/3/24
We propose a novel probabilistic method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algorithm that allows to detect obje...
A lower bound for the Graver complexity of the incidence matrix of a complete bipartite graph
algebraic statistics contingency table three-way transportation pro-gram
2011/3/24
We give an exponential lower bound for the Graver complexity of the incidence matrix of a complete bipartite graph of arbitrary size. Our result is a generalization of the result by Berstein and Onn (...
Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting
Accounting for Calibration Uncertainties Effective Areas in Spectral Fitting X-ray Analysis
2011/3/25
While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a ...
Lack of confidence in ABC model choice
Lack of confidence ABC model choice Approximate Bayesian computation
2011/3/24
Approximate Bayesian computation (ABC) have become a essential tool for the analysis of complex stochastic models. Earlier, Grelaud et al. (2009) advocated the use of ABC for Bayesian model choice in ...
Robust Estimation through Schoenberg transformations
Correspondence Analysis Euclidean distances Huber func-tion Huygens principles M-estimators Schoenberg transformations Tukey bisquare
2011/3/24
Schoenberg transformations, mapping Euclidean configurations into Euclidean configurations, define in turn a transformed inertia, whose minimization produces robust location estimates. The procedure o...
Perfect Simulation for Mixtures with Known and Unknown Number of components
Bounding chains Dirichlet process Gibbs sampling Mixtures Optimization Perfect Sam-pling
2011/3/24
We propose and develop a novel and effective perfect sampling methodology for simulating from posteriors corresponding to mixtures with either known (fixed) or unknown number of components. For the la...