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Monetary Policy under Uncertainty
 IN MICROFOUNDED MACROECONOMETRIC MODELS,Â NBER MACROECONOMICS ANNUAL
, 2005
"... We use a microfounded macroeconometric modeling framework to investigate the design of monetary policy when the central bank faces uncertainty about the true structure of the economy. We apply Bayesian methods to estimate the parameters of the baseline specification using postwar U.S. data and then ..."
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Cited by 245 (14 self)
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We use a microfounded macroeconometric modeling framework to investigate the design of monetary policy when the central bank faces uncertainty about the true structure of the economy. We apply Bayesian methods to estimate the parameters of the baseline specification using postwar U.S. data
Ranking of Closeness Centrality for LargeScale Social Networks
"... Abstract. Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate ..."
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Cited by 26 (2 self)
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Abstract. Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate
Nuclear norm penalization and optimal rates for noisy low rank matrix completion.
 Annals of Statistics,
, 2011
"... AbstractThis paper deals with the trace regression model where n entries or linear combinations of entries of an unknown m1 × m2 matrix A0 corrupted by noise are observed. We propose a new nuclear norm penalized estimator of A0 and establish a general sharp oracle inequality for this estimator for ..."
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Cited by 107 (7 self)
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for arbitrary values of n, m1, m2 under the condition of isometry in expectation. Then this method is applied to the matrix completion problem. In this case, the estimator admits a simple explicit form and we prove that it satisfies oracle inequalities with faster rates of convergence than in the previous works
Benchmarking attribute selection techniques for discrete class data mining
 IEEE Trans. Knowl. Data Eng
"... Data engineering is generally considered to be a central issue in the development of data mining applications. The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of i ..."
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Cited by 184 (2 self)
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. This leads to a large number of possible permutations and has led to a situation where very few benchmark studies have been conducted. This paper presents a benchmark comparison of several attribute selection methods. All the methods produce an attribute ranking, a useful devise for isolating the individual
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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configurations and kspace sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to onehalf using a two
Distributed Assessment of the Closeness Centrality Ranking in Complex Networks
"... We propose a method for the Distributed Assessment of the Closeness CEntrality Ranking (DACCER) in complex networks. DACCER computes centrality based only on localized information restricted to a given neighborhood around each node, thus not requiring full knowledge of the network topology. We show ..."
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Cited by 4 (0 self)
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We propose a method for the Distributed Assessment of the Closeness CEntrality Ranking (DACCER) in complex networks. DACCER computes centrality based only on localized information restricted to a given neighborhood around each node, thus not requiring full knowledge of the network topology. We show
Whom You Know Matters: Venture Capital Networks and Investment Performance,
 Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm'slength, spotmarket transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
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Cited by 138 (8 self)
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adjacency matrices over trailing fiveyear windows. Using these matrices, we construct five centrality measures based on three popular concepts of centrality: Degree, closeness, and betweenness. Using a numerical example, the Appendix shows in detail how these centrality measures are constructed. Here, we
A Closed Form Solution to Robust Subspace Estimation and Clustering
"... We consider the problem of fitting one or more subspaces to a collection of data points drawn from the subspaces and corrupted by noise/outliers. We pose this problem as a rank minimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean, selfexpressive, low ..."
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Cited by 43 (4 self)
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We consider the problem of fitting one or more subspaces to a collection of data points drawn from the subspaces and corrupted by noise/outliers. We pose this problem as a rank minimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean, selfexpressive, lowrank
Entailed Ranking Arguments
, 2002
"... An ‘elementary ranking condition ’ (ERC) embodies the kind of restrictions imposed by a comparison between a desired optimum and a single competitor. All entailments between elementary ranking conditions can be ascertained through three simple formal rules; one of them introduces a method of argumen ..."
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Cited by 35 (4 self)
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An ‘elementary ranking condition ’ (ERC) embodies the kind of restrictions imposed by a comparison between a desired optimum and a single competitor. All entailments between elementary ranking conditions can be ascertained through three simple formal rules; one of them introduces a method
Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals
"... Abstract—Distributed algorithms are developed for optimal estimation of stationary random signals and smoothing of (even nonstationary) dynamical processes based on generally correlated observations collected by ad hoc wireless sensor networks (WSNs). Maximum a posteriori (MAP) and linear minimum me ..."
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Cited by 99 (7 self)
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meansquare error (LMMSE) schemes, well appreciated for centralized estimation, are shown possible to reformulate for distributed operation through the iterative (alternatingdirection) method of multipliers. Sensors communicate with singlehop neighbors their individual estimates as well
Results 1  10
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