Results 1  10
of
4,466,096
Determining the Number of Factors in Approximate Factor Models
, 2000
"... In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors c ..."
Abstract

Cited by 561 (30 self)
 Add to MetaCart
In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors
SCRIBE: A largescale and decentralized applicationlevel multicast infrastructure
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (JSAC
, 2002
"... This paper presents Scribe, a scalable applicationlevel multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peertopeer object location and routing substrate overlayed on the Internet, ..."
Abstract

Cited by 653 (29 self)
 Add to MetaCart
This paper presents Scribe, a scalable applicationlevel multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peertopeer object location and routing substrate overlayed on the Internet
Large Numbers in Computing and
"... We present an overview of large numbers within mathematics and computing. Particular emphasis is put on the problem of large number notation in the mathematical attempt to get closer to infinity. ..."
Abstract
 Add to MetaCart
We present an overview of large numbers within mathematics and computing. Particular emphasis is put on the problem of large number notation in the mathematical attempt to get closer to infinity.
Large steps in cloth simulation
 SIGGRAPH 98 Conference Proceedings
, 1998
"... The bottleneck in most cloth simulation systems is that time steps must be small to avoid numerical instability. This paper describes a cloth simulation system that can stably take large time steps. The simulation system couples a new technique for enforcing constraints on individual cloth particle ..."
Abstract

Cited by 577 (5 self)
 Add to MetaCart
as well. The implicit integration method generates a large, unbanded sparse linear system at each time step which is solved using a modified conjugate gradient method that simultaneously enforces particles ’ constraints. The constraints are always maintained exactly, independent of the number of conjugate
Forecasting using principal components from a large number of predictors
 Journal of the American Statistical Association
"... This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecas ..."
Abstract

Cited by 232 (5 self)
 Add to MetaCart
in the presence of time variation in the factor model. KEY WORDS: Factor models; Forecasting; Principal components. This article considers forecasting one series using a large number of predictor series. In macroeconomic forecasting, for example, the number of candidate predictor series (N) can be very large
New Look at the Large Numbers
 Int. J. Theor. Phys
, 1986
"... A new interpretation for the large number hypothesis is given, referring to the close connection between the BekensteinHawking entropy and Weizsäckers ur theory. 1. ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
A new interpretation for the large number hypothesis is given, referring to the close connection between the BekensteinHawking entropy and Weizsäckers ur theory. 1.
CURE: An Efficient Clustering Algorithm for Large Data sets
 Published in the Proceedings of the ACM SIGMOD Conference
, 1998
"... Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering ..."
Abstract

Cited by 723 (5 self)
 Add to MetaCart
clustering algorithm called CURE that is more robust to outliers, and identifies clusters having nonspherical shapes and wide variances in size. CURE achieves this by representing each cluster by a certain fixed number of points that are generated by selecting well scattered points from the cluster
Large margin methods for structured and interdependent output variables
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract

Cited by 623 (12 self)
 Add to MetaCart
to accomplish this, we propose to appropriately generalize the wellknown notion of a separation margin and derive a corresponding maximummargin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm
Results 1  10
of
4,466,096