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608
SMOOTHED MAXIMUM SCORE ESTIMATOR
, 1996
"... The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and asymptotically normal under weak distributional assumptions. However, the differences between the true and nominal levels of tests based on smoothed maximum score estimates can be very large i ..."
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The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and asymptotically normal under weak distributional assumptions. However, the differences between the true and nominal levels of tests based on smoothed maximum score estimates can be very large
SoftRank: Optimizing NonSmooth Rank Metrics
, 2008
"... We address the problem of learning large complex ranking functions. Most IR applications use evaluation metrics that depend only upon the ranks of documents. However, most ranking functions generate document scores, which are sorted to produce a ranking. Hence IR metrics are innately nonsmooth with ..."
Abstract

Cited by 54 (2 self)
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We address the problem of learning large complex ranking functions. Most IR applications use evaluation metrics that depend only upon the ranks of documents. However, most ranking functions generate document scores, which are sorted to produce a ranking. Hence IR metrics are innately nonsmooth
Learning to rank with nonsmooth cost functions
 In Advances in Neural Information Processing Systems (NIPS) 20
, 2006
"... The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query. Thus, the derivatives of the cost with respect to the model parameters are either zero ..."
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Cited by 176 (11 self)
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The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query. Thus, the derivatives of the cost with respect to the model parameters are either
G: Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method
 Siam J Sci Statist Comp
, 1991
"... Abstract. The (modified) Newton method is adapted to optimize generalized cross validation (GCV) and generalized maximum likelihood (GML) scores with multiple smoothing parameters. The main concerns in solving the optimization problem are the speed and the reliability of the algorithm, as well as th ..."
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Cited by 60 (10 self)
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Abstract. The (modified) Newton method is adapted to optimize generalized cross validation (GCV) and generalized maximum likelihood (GML) scores with multiple smoothing parameters. The main concerns in solving the optimization problem are the speed and the reliability of the algorithm, as well
On a Small Sample Adjustment for the Profile Score Function in Semiparametric Smoothing Models
"... We consider the prole score function in models with smooth and parametric components. If local respectively weighted likelihood estimation is used for tting the smooth component, the resulting prole likelihood estimate for the parametric component is asymptotically e cient as shown in Severini & ..."
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Cited by 1 (0 self)
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We consider the prole score function in models with smooth and parametric components. If local respectively weighted likelihood estimation is used for tting the smooth component, the resulting prole likelihood estimate for the parametric component is asymptotically e cient as shown in Severini
A Smoothed Maximum Score Estimator for Multinomial Discrete Choice Models
, 2012
"... We propose a semiparametric estimator for multinomial discrete choice models. The term “semiparametric ” refers to the fact that we do not specify a particular functional form for the error term in the random utility function and we allow for heteroskedasticity and serial correlation. Despite being ..."
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semiparametric, the rate of convergence of the smoothed maximum score estimator is not affected by the number of alternative choices and does not suffer from the “curse of dimensionality”. We show the strong consistency and asymptotic normality of the smoothed maximum score estimator for multinomial discrete
Kernel Weighted Smoothed Maximum Score Estimation for Applied Work
, 2011
"... The endogenous binary response model frequently arises in economic applications when a covariate is correlated with the error term in the latent equation due to data limitations. Applied workers generally address endogeneity using the principle of Maximum Likelihood (ML) which imposes stringent para ..."
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The endogenous binary response model frequently arises in economic applications when a covariate is correlated with the error term in the latent equation due to data limitations. Applied workers generally address endogeneity using the principle of Maximum Likelihood (ML) which imposes stringent parametric assumptions. These ML estimators are inconsistent if the posited parametrization is incorrect which can translate in practice into aberrant results contradicting economic theory. Semiparametric estimators have been developed imposing weaker distributional assumptions. Some semiparametric techniques permit inferences from data but restrict heteroscedasticity which may furnish deceptive results. Other semiparametric techniques can accommodate almost any heteroscedasticity but forbid inferences. This article summarizes two new estimation techniques which allow for inferences under general heteroscedasticity conditions. Some Monte Carlo experiments are conducted highlighting the robust advantage of these estimators. Finally, these estimation techniques are applied to assess the effect of education on maternal pregnancy smoking using the 1988 National Health Interview Survey.
Functional data analysis for sparse longitudinal data.
 Journal of the American Statistical Association
, 2005
"... We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data a ..."
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Cited by 123 (24 self)
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analysis requires a large number of regularly spaced measurements per subject. We assume that the repeated measurements are located randomly with a random number of repetitions for each subject and are determined by an underlying smooth random (subjectspecific) trajectory plus measurement errors. Basic
Pagelevel template detection via isotonic smoothing
 In Proc. of In Conference on World Wide Web
, 2007
"... We develop a novel framework for the pagelevel template detection problem. Our framework is built on two main ideas. The first is the automatic generation of training data for a classifier that, given a page, assigns a templateness score to every DOM node of the page. The second is the global smoot ..."
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Cited by 34 (3 self)
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smoothing of these pernode classifier scores by solving a regularized isotonic regression problem; the latter follows from a simple yet powerful abstraction of templateness on a page. Our extensive experiments on humanlabeled test data show that our approach detects templates effectively.
Smoothness and Structure Learning by Proxy
"... As data sets grow in size, the ability of learning methods to find structure in them is increasingly hampered by the time needed to search the large spaces of possibilities and generate a score for each that takes all of the observed data into account. For instance, Bayesian networks, the model c ..."
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here that the use of such a proxy is wellfounded, as we can bound the smoothness of a commonlyused scoring function for Bayesian network structure learning. We show here that, compared to an identical search strategy using the network’s exact scores, our proxybased search is able to get equivalent
Results 1  10
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608