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Lag length selection and the construction of unit root tests with good size and power
 Econometrica
, 2001
"... It is widely known that when there are errors with a movingaverage root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We conside ..."
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Cited by 557 (14 self)
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consider a class of Modified Information Criteria (MIC) with a penalty factor that is sample dependent. It takes into account the fact that the bias in the sum of the autoregressive coefficients is highly dependent on k and adapts to the type of deterministic components present. We use a local asymptotic
A Simple Estimator of Cointegrating Vectors in Higher Order Cointegrated Systems
 ECONOMETRICA
, 1993
"... Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. T ..."
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Cited by 523 (3 self)
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Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions
Boosting and differential privacy
, 2010
"... Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacypreserving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates of the resp ..."
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Cited by 648 (14 self)
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Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacypreserving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates
The strength of weak learnability
 MACHINE LEARNING
, 1990
"... This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high prob ..."
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Cited by 870 (26 self)
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This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high
Fast deterministic distributed algorithms for sparse spanners
 IN 13 TH INTERNATIONAL COLLOQUIUM ON STRUCTURAL INFORMATION & COMMUNICATION COMPLEXITY (SIROCCO
, 2006
"... This paper concerns the efficient construction of sparse and low stretch spanners for unweighted arbitrary graphs with n nodes. All previous deterministic distributed algorithms, for constant stretch spanner of o(n²) edges, have a running time Ω(n^ɛ) for some constant ɛ > 0 depending on the stret ..."
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Cited by 9 (5 self)
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This paper concerns the efficient construction of sparse and low stretch spanners for unweighted arbitrary graphs with n nodes. All previous deterministic distributed algorithms, for constant stretch spanner of o(n²) edges, have a running time Ω(n^ɛ) for some constant ɛ > 0 depending
Bullet: High Bandwidth Data Dissemination Using an Overlay Mesh
, 2003
"... In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes selforganize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burd ..."
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Cited by 425 (22 self)
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deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to selforganize into a high bandwidth overlay mesh. We construct Bullet around the insight that data
Improved methods for tests of longrun abnormal stock returns
 Journal of Finance
, 1999
"... We analyze tests for longrun abnormal returns and document that two approaches yield wellspecified test statistics in random samples. The first uses a traditional event study framework and buyandhold abnormal returns calculated using carefully constructed reference portfolios. Inference is based ..."
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Cited by 372 (12 self)
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We analyze tests for longrun abnormal returns and document that two approaches yield wellspecified test statistics in random samples. The first uses a traditional event study framework and buyandhold abnormal returns calculated using carefully constructed reference portfolios. Inference
Topologicallyaware overlay construction and server selection
, 2002
"... A number of largescale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the applicationlevel connectivity between ..."
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Cited by 340 (3 self)
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A number of largescale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the applicationlevel connectivity
Light Spanners∗
"... A tspanner of a weighted undirected graph G = (V,E), is a subgraph H such that dH(u, v) ≤ t · dG(u, v) for all u, v ∈ V. The sparseness of the spanner can be measured by its size (the number of edges) and weight (the sum of all edge weights), both being important measures of the spanner’s quality – ..."
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A tspanner of a weighted undirected graph G = (V,E), is a subgraph H such that dH(u, v) ≤ t · dG(u, v) for all u, v ∈ V. The sparseness of the spanner can be measured by its size (the number of edges) and weight (the sum of all edge weights), both being important measures of the spanner’s quality
Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces
, 1993
"... We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are highdim ..."
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Cited by 357 (5 self)
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dimensional Euclidian settings in which the distribution of data is in some sense of lower dimension and embedded in the space. The vptree (vantage point tree) is introduced in several forms, together with associated algorithms, as an improved method for these difficult search problems. Tree construction executes
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