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A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/23
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
Ornstein-Uhlenbeck type processes with heavy distribution tails
Ornstein-Uhlenbeck process heavy tails regular variation rank cor-relation Gauss copula log returns modelling
2011/3/25
We consider a transformed Ornstein-Uhlenbeck process model that can be a good candidate for modelling real-life processes characterized by a combination of time-reverting behaviour with heavy distribu...
Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameteri...
Instant Replay: Investigating statistical Analysis in Sports
Instant Replay Sports Investigating statistical Analysis
2011/3/25
Technology has had an unquestionable impact on the way people watch sports. As technology has evolved, so too has the knowledge of a casual sports fan. A direct result of this evolution is the amount ...
Probabilistic analysis of the human transcriptome with side information
data integration exploratory data analysis functional genomics probabilistic modeling transcriptomics
2011/3/25
Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput...
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...