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Regression rankscores tests in R
"... R. Koenker a G. Basset (1978) proposed the regression quantiles as any generalization of usual quantiles to linear regression model. They characterized the regression quantile as the solution of the linear program. Gutenbrunner and Jurečková (1992) called the componenents of the optimal solution of ..."
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of dual problem as the regression rank scores. They showed that many aspects of the duality of order statistics and ranks in the location model generalize naturally to the linear model. Gutenbrunner and Jurečková (1992) proposed some tests based on regression rank scores generated by truncated score
Ranking Score Vectors of Tournaments
, 2011
"... Part of the Mathematics Commons This Report is brought to you for free and open access by the Graduate Studies at ..."
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Part of the Mathematics Commons This Report is brought to you for free and open access by the Graduate Studies at
Regression rank scores in nonlinear models
"... Abstract: Consider the nonlinear regression model Yi = g(xi, θ)+ei, i =1,...,n (1) with xi ∈ R k, θ =(θ0,θ1,...,θp) ′ ∈ Θ (compact in R p+1), where g(x, θ) = θ0 +˜g(x,θ1,...,θp) is continuous, twice differentiable in θ and monotone in components of θ. Following Gutenbrunner and Jurečková (1992) an ..."
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Cited by 4 (0 self)
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) and Jurečková and Procházka (1994), we introduce regression rank scores for model (1), and prove their asymptotic properties under some regularity conditions. As an application, we propose some tests in nonlinear regression models with nuisance parameters.
TopicSensitive PageRank
, 2002
"... In the original PageRank algorithm for improving the ranking of searchquery results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search resu ..."
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Cited by 535 (10 self)
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results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate queryspecific importance scores for pages at query
Very Low Scoring Score Rank Score Rank
"... UACUGAC 9.2321 2 8.7037 2 UAGUAAC 8.0803 3 5.8523 37 UAGUGAC 7.8172 4 5.1505 69 UACUAAU 7.4953 5 7.4166 9 ..."
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UACUGAC 9.2321 2 8.7037 2 UAGUAAC 8.0803 3 5.8523 37 UAGUGAC 7.8172 4 5.1505 69 UACUAAU 7.4953 5 7.4166 9
Tests of Linear Hypotheses Based on Regression Rank Scores
 J. Nonparametric Statistics
, 1993
"... We propose a general class of asymptotically distributionfree tests of a linear hypothesis in the linear regression model. The tests are based on regression rank scores, recently introduced by Gutenbrunner and Jurec̀ ́ kova ́ (1992) as dual variables to the regression quantiles of Koenker and Basse ..."
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Cited by 42 (7 self)
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We propose a general class of asymptotically distributionfree tests of a linear hypothesis in the linear regression model. The tests are based on regression rank scores, recently introduced by Gutenbrunner and Jurec̀ ́ kova ́ (1992) as dual variables to the regression quantiles of Koenker
Significance Analysis of Microarrays using Rank Scores
"... The Significance Analysis of Microarrays (SAM) software is a very practical tool for detecting significantly expressed genes and controlling the proportion of falsely detected genes, the False Discovery Rate (FDR). However, SAM tends to find biased estimates of the FDR. We show that the same method ..."
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with the data replaced by rank scores does not have this tendency. We discuss the choice of the rank score function in view of the power of this nonparametric multiple testing procedure. Moreover, we introduce a testing formalization of the popular 2fold rule. This testing procedure is more selective than
Learning to rank using gradient descent
 In ICML
, 2005
"... We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data f ..."
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Cited by 510 (17 self)
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We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data
ASYMPTOTIC EQUIVALENCE OF REGRESSION RANK SCORES ESTIMATORS AND RESTIMATORS IN LINEAR MODELS
, 1992
"... The classical Restimators in linear models are computationally more cumbersome than the regression rank scores estimators. Under appropriate regularity conditions, both the methods are shown to be asymptotically equivalent. A coordinatewise modification of regression rank scores estimators is also ..."
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The classical Restimators in linear models are computationally more cumbersome than the regression rank scores estimators. Under appropriate regularity conditions, both the methods are shown to be asymptotically equivalent. A coordinatewise modification of regression rank scores estimators is also
Rank Aggregation Methods for the Web
, 2010
"... We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building metasearch engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. Wed ..."
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Cited by 473 (6 self)
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We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building metasearch engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations
Results 1  10
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