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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...
A Global BMI Algorithm Based on the Generalized Benders Decomposition
Linear matrix inequalities Robust control Numerical Methods
2015/7/13
We present a new algorithm for the global solution of optimization problems involving bilinear matrix inequalities (BMIs). The method is based on a technique known in large-scale and global optimizati...
Simultaneous Routing and Resource Allocation via Dual Decomposition
Communication systems networks optimization methods resource allocation routing
2015/7/10
In wireless data networks the optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communications resources (such as powers and bandwidths) to the...
A Decomposition Approach to Distributed Analysis of Networked Systems
Decomposition Approach Distributed Analysis Networked Systems
2015/7/10
We present a simple distributed algorithm for analyzing well-posedness and stability of a system composed of different sub-units, interconnected over an arbitrary graph. The procedure consists in solv...
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...
Distributed estimation via dual decomposition
Stochastic control Model predictive control Linear matrix inequality
2015/7/9
We develop computational bounds on performance for causal state feedback stochastic control with linear dynamics, arbitrary noise distribution, and arbitrary input constraint set. This can be very use...
The time-frequency and time-scale communities have recently developed a large number of
overcomplete waveform dictionaries—stationary wavelets, wavelet packets, cosine packets,
chirplets, and warple...
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...