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419,473
The group Lasso for logistic regression
 Journal of the Royal Statistical Society, Series B
, 2008
"... Summary. The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regressi ..."
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Cited by 276 (11 self)
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Summary. The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic
Regularization networks and support vector machines
 Advances in Computational Mathematics
, 2000
"... Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization a ..."
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Cited by 366 (38 self)
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Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
, 2004
"... This paper presents a new approach to estimation and inference in panel data models with a multifactor error structure where the unobserved common factors are (possibly) correlated with exogenously given individualspecific regressors, and the factor loadings differ over the cross section units. The ..."
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Cited by 383 (44 self)
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. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by (weighted) cross sectional averages of the dependent variable and the individual specific regressors. Two different but related problems are addressed: one
Prioritizing test cases for regression testing
 IEEE Transactions on Software Engineering
, 2001
"... Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection — a measure of how quickly faults are detected within the testing process ..."
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Cited by 234 (15 self)
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following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. In this paper, we describe several techniques for using test execution information to prioritize test cases for regression
Logistic Regression, AdaBoost and Bregman Distances
, 2000
"... We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt al ..."
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Cited by 259 (45 self)
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We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex but the memor ..."
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Cited by 334 (2 self)
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but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can
Credibility in the regression case revisited (A late tribute to Charles A
 Hachemeister). ASTIN Bulletin
, 1997
"... ABSTRACT Many authors have observed that Hachemelsters Regression Model for Credlbdlty ff apphed to simple linear regresstonleads to unstmsfactory credibility matrices they typically 'max up" the regressmn parameters and m particular lead to regression hnes that seem 'out of range& ..."
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Cited by 1 (1 self)
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hne at the barycenter of tmle. With these definmons regression parameters which are uncorrelated m the collective can be estimated separately by standard one d~menmonal credibility techmques A slmdar convement reparalnetnzat~on can also be achieved in the general regresstun case The good choice
Comparing Wealth Effects: The Stock Market vs. the Housing Market,” NBER Working Paper No
, 2001
"... We examine the link between increases in housing wealth, financial wealth, and consumer spending. We rely upon a panel of 14 countries observed annually for various periods during the past 25 years and a panel of U.S. states observed quarterly during the 1980s and 1990s. We impute the aggregate valu ..."
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Cited by 304 (11 self)
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value of owneroccupied housing, the value of financial assets, and measures of aggregate consumption for each of the geographic units over time. We estimate regressions relating consumption to income and wealth measures, finding a statistically significant and rather large effect of housing wealth upon
Nonparametric regression using Bayesian variable selection
 Journal of Econometrics
, 1996
"... This paper estimates an additive model semiparametrically, while automatically selecting the significant independent variables and the app~opriatc power transformation of the dependent variable. The nonlinear variables arc modeled as regression splincs, with significant knots selected fiom a large ..."
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Cited by 215 (17 self)
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This paper estimates an additive model semiparametrically, while automatically selecting the significant independent variables and the app~opriatc power transformation of the dependent variable. The nonlinear variables arc modeled as regression splincs, with significant knots selected fiom a
Boosting with the L_2Loss: Regression and Classification
, 2001
"... This paper investigates a variant of boosting, L 2 Boost, which is constructed from a functional gradient descent algorithm with the L 2 loss function. Based on an explicit stagewise re tting expression of L 2 Boost, the case of (symmetric) linear weak learners is studied in detail in both regressi ..."
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Cited by 208 (17 self)
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This paper investigates a variant of boosting, L 2 Boost, which is constructed from a functional gradient descent algorithm with the L 2 loss function. Based on an explicit stagewise re tting expression of L 2 Boost, the case of (symmetric) linear weak learners is studied in detail in both
Results 11  20
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419,473