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882,802
PatternDependent Noise Prediction in SignalDependent Noise
, 2001
"... Maximum and nearmaximum likelihood sequence detectors in signaldependent noise are discussed. It is shown that the linear prediction viewpoint allows a very simple derivation of the branch metric expression that has previously been shown as optimum for signaldependent Markov noise. The resulting ..."
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Cited by 22 (3 self)
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Maximum and nearmaximum likelihood sequence detectors in signaldependent noise are discussed. It is shown that the linear prediction viewpoint allows a very simple derivation of the branch metric expression that has previously been shown as optimum for signaldependent Markov noise. The resulting
Noise Trader Risk in Financial Markets
 Jolurnial of Political Economy
, 1990
"... We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders ’ beliefs creates a risk in the price of the asset that deters rational ..."
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Cited by 858 (23 self)
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We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders ’ beliefs creates a risk in the price of the asset that deters rational
Nonlinear total variation based noise removal algorithms
, 1992
"... A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the g ..."
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Cited by 2270 (52 self)
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A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using
On Distributed Detection In Dependent Noise
 PROCEEDINGS OF THE 26TH ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS
, 1992
"... This paper considers discretetime distributed detection of a constant signal in dependent noise. A number of sensors transmit transformations of their observations to a central processor where a final decision is then declared. Two types of transformations are investigated: Decision fusion, where t ..."
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Cited by 1 (1 self)
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This paper considers discretetime distributed detection of a constant signal in dependent noise. A number of sensors transmit transformations of their observations to a central processor where a final decision is then declared. Two types of transformations are investigated: Decision fusion, where
Searching with Measurement Dependent Noise
"... Abstract—Consider a target moving with a constant velocity on a unitcircumference circle, starting from an arbitrary location. To acquire the target, any region of the circle can be probed for its presence, but the associated measurement noise increases with the size of the probed region. We are in ..."
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dependent noise incurs a multiplicative gap between adaptive search and nonadaptive search. Moreover, our adaptive scheme attains the optimal ratereliability tradeoff. We further show that for optimal nonadaptive search, accounting for an unknown velocity incurs a factor of two in rate. I.
ON DISTRIBUTED DETECTION IN DEPENDENT NOISE
"... This paper considers discretetime distributed detection of a constant signal in dependent noise. A number of sensors transmit transformations of their observations to a central processor where a final decision is then declared. Two types of transformations are investigated: Decision fusion, where t ..."
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This paper considers discretetime distributed detection of a constant signal in dependent noise. A number of sensors transmit transformations of their observations to a central processor where a final decision is then declared. Two types of transformations are investigated: Decision fusion, where
Financial Dependence and Growth
 American Economic Review
, 1998
"... This paper examines whether nancial development facilitates economic growth by scrutinizing one rationale for such a relationship; that nancial development reduces the costs of external nance to rms. Speci cally, we ask whether industrial sectors that are relatively more in need of external nance de ..."
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Cited by 1043 (29 self)
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This paper examines whether nancial development facilitates economic growth by scrutinizing one rationale for such a relationship; that nancial development reduces the costs of external nance to rms. Speci cally, we ask whether industrial sectors that are relatively more in need of external nance develop disproportionately faster in countries with more developed nancial markets. We nd this to be true in a large sample of countries over the 1980s. We show this result is unlikely to be driven by omitted variables, outliers, or reverse causality. (JEL O4, F3, G1) A large literature, dating at least as far back as Joseph A. Schumpeter (1911), emphasizes the positive in uence of the development of a country's nancial sector on the level and the rate of growth of its per capita income. The argument essentially is that the services the nancial sector provides { of reallocating capital to the highest value use without substantial risk of loss through moral hazard, adverse selection, or transactions costs { are an essential catalyst of economic growth. Empirical work seems consistent with this argument. For example, on the
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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that has been contaminated with additive noise, the goal is to identify which elementary signals participated and to approximate their coefficients. Although many algorithms have been proposed, there is little theory which guarantees that these algorithms can accurately and efficiently solve the problem
FeatureRich PartofSpeech Tagging with a Cyclic Dependency Network
 IN PROCEEDINGS OF HLTNAACL
, 2003
"... We present a new partofspeech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective ..."
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Cited by 660 (23 self)
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We present a new partofspeech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii
Random forests
 Machine Learning
, 2001
"... Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the fo ..."
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Cited by 3433 (2 self)
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Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees
Results 1  10
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