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The backbone decomposition for spatially dependent supercritical superprocesses
Superprocesses N-measure backbone decomposition
2016/1/20
Consider any supercritical Galton-Watson process which may become extinct with positive probability. It is a well-understood and intuitively obvious phenomenon that,on the survival set, the process ma...
Distributed Estimation via Dual Decomposition
Distributed Estimation via Dual Decomposition
2015/7/10
The focus of this paper is to develop a framework for distributed estimation via convex optimization. We deal with a network of complex sensor subsystems with local estimation and signal processing. M...
Modelling time and vintage variability in retail credit portfolios: the decomposition approach
Age-period-cohort default Exogeneous EMV model Forecasting Macroeco-nomic Statistical model Vintage
2013/6/14
In this paper, we consider the problem of modelling historical data on retail credit portfolio performance, with a view to forecasting future performance, and facilitating strategic decision making. W...
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Convex Tensor Decomposition Structured Schatten Norm Regularization
2013/4/28
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tens...
Variance estimation for Brier Score decomposition
Variance estimation Brier Score decomposition
2013/4/28
The Brier Score is a widely-used criterion to assess the quality of probabilistic predictions of binary events. The expectation value of the Brier Score can be decomposed into the sum of three compone...
Cramer-Rao-Induced Bounds for CANDECOMP/PARAFAC tensor decomposition
CANDECOMP/PARAFAC Cramer-Rao-Induced tensor decomposition Bounds
2012/11/22
This paper presents a Cramer-Rao lower bound (CRLB) on the variance of unbiased estimates of factor matrices in Canonical Polyadic (CP) or CANDECOMP/PARAFAC (CP) decompositions of a tensor from noisy ...
Variance Decomposition and Replication In Scrabble: When You Can Blame Your Tiles?
Variance Decomposition eplication In Scrabble
2011/7/19
In the game of Scrabble, letter tiles are drawn uniformly at random from a bag. The variability of possible draws as the game progresses is a source of variation that makes it more likely for an infer...
A Generalized Least Squares Matrix Decomposition
matrix decomposition,singular value decomposition,transposable data,principal components analysis,sparse principal components analysis,functional prin-cipal components analysis,spatio-temporal data
2011/3/21
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
A Generalized Least Squares Matrix Decomposition
matrix decomposition singular value decomposition transposable data principal components analysis, sparse principal components analysis functional prin-cipal components analysis spatio-temporal data
2011/3/23
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
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...
Adaptive estimation of covariance matrices via Cholesky decomposition
Covariance matrix banding Cholesky decomposition
2010/10/19
This paper studies the estimation of a large covariance matrix. We introduce a novel procedure called ChoSelect based on the Cholesky factor of the inverse covariance. This method uses a dimension red...
On the construction of the Wold decomposition for non-stationary stochastic processes
the construction of the Wold decomposition non-stationary stochastic processes
2009/9/24
On the construction of the Wold decomposition for non-stationary stochastic processes。
Decomposition of convolution semigroups on groups and the 0-1 law
Decomposition of convolution semigroups groups and the 0-1 law
2009/9/22
Let (X (t))a,o be a stochastically continuous symmetric
Levy process with values in a complete separable group G. We denote
by h),,,, the semigroup of one-dimensional distributions of X(t). Suppose
...
TOWARDS A GENERAL DOBB-MEYER DECOMPOSITION THEOREM
DoobMeyer decomposition theorem predictable compensator processes with finite energy martingales
2009/9/18
Both the DoobMeyer and Graversen-Rao decomposition
theorems can be proved following an approach based on predictable
compensators of discretizations and weak-l1 technique, which
was developed by K....
Decomposition of neuronal assembly activity via empirical de-Poissonization
asymptotics compound Poisson process empirical characteristic function higher-order interactions jump measure
2009/9/16
Consider a compound Poisson process with jump measure $nu$ supported by finitely many positive integers. We propose a method for estimating $nu$ from a single, equidistantly sampled trajectory and dev...