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589
A tutorial on support vector regression
, 2004
"... In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing ..."
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Cited by 865 (3 self)
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In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.
Kernel independent component analysis
 Journal of Machine Learning Research
, 2002
"... We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical propert ..."
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Cited by 464 (24 self)
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We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety of source distributions, showing that our algorithms outperform many of the presently known algorithms. 1.
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
, 2000
"... We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for diff ..."
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Cited by 416 (35 self)
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We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the pretreatmentvariables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pretreatment variables, the propensity score, also removes the entire bias associated with differences in pretreatment variables. Thus it is possible to obtain unbiased estimates of the treatment effect without conditioning on a possibly highdimensional vector of pretreatment variables. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency. We show that weighting with the inverse of a nonparametric estimate of the propensity score, rather than the true propensity scor...
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
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Cited by 185 (19 self)
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Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method of sieves provides one way to tackle such complexities by optimizing an empirical criterion function over a sequence of approximating parameter spaces, called sieves, which are significantly less complex than the original parameter space. With different choices of criteria and sieves, the method of sieves is very flexible in estimating complicated econometric models. For example, it can simultaneously estimate the parametric and nonparametric components in seminonparametric models with or without constraints. It can easily incorporate prior information, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. This chapter describes estimation of seminonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
The incidental parameter problem since 1948
 JOURNAL OF ECONOMETRICS 95 (2000) 391413
, 2000
"... This paper was written to mark the 50th anniversary of Neyman and Scott's Econometrica paper defining the incidental parameter problem. It surveys the history both of the paper and of the problem in the statistics and econometrics literature. ..."
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Cited by 124 (0 self)
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This paper was written to mark the 50th anniversary of Neyman and Scott's Econometrica paper defining the incidental parameter problem. It surveys the history both of the paper and of the problem in the statistics and econometrics literature.
Efficient semiparametric estimation of quantile treatment effects
, 2003
"... This paper presents calculations of semiparametric efficiency bounds for quantile treatment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric e ..."
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Cited by 121 (5 self)
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This paper presents calculations of semiparametric efficiency bounds for quantile treatment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric estimation and a computation of the difference between the solutions of two distinct minimization problems. RootN consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound is shown for one of the three estimators. In the final part of the paper, an empirical application to a job training program reveals the importance of heterogeneous treatment effects, showing that for this program the effects are concentrated in the upper quantiles of the earnings distribution.
Adjusting for nonignorable dropout using semiparametric nonresponse models (with discussion
 Journal of the American Statistical Association
, 1999
"... Consider a study whose design calls for the study subjects to be followed from enrollment (time t = 0) to time t = T,at which point a primary endpoint of interest Y is to be measured. The design of the study also calls for measurements on a vector V(t) of covariates to be made at one or more times t ..."
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Cited by 120 (14 self)
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Consider a study whose design calls for the study subjects to be followed from enrollment (time t = 0) to time t = T,at which point a primary endpoint of interest Y is to be measured. The design of the study also calls for measurements on a vector V(t) of covariates to be made at one or more times t during the interval [0,T). We are interested in making inferences about the marginal mean µ0 of Y when some subjects drop out of the study at random times Q prior to the common fixed end of followup time T. The purpose of this article is to show how to make inferences about µ0 when the continuous dropout time Q is modeled semiparametrically and no restrictions are placed on the joint distribution of the outcome and other measured variables. In particular, we consider two models for the conditional hazard of dropout given ( ¯ V(T), Y), where ¯ V(t) denotes the history of the process V(t) through time t, t ∈ [0,T). In the first model, we assume that λQ(t  ¯ V(T), Y) = λ0(t  ¯ V(t)) exp(α0Y), where α0 is a scalar parameter and λ0(t  ¯ V(t)) is an unrestricted positive function of t and the process ¯ V(t). When the process ¯ V(t) is high dimensional, estimation in this model is not feasible with moderate sample sizes, due to the curse of dimensionality. For such situations, we consider a second model that imposes the additional restriction that λ0(t  ¯ V(t)) = λ0(t) exp(γ ′ 0W(t)), where λ0(t) is an unspecified baseline hazard function, W(t) = w(t, ¯ V(t)), w(·, ·) is a known function that maps (t, ¯ V(t)) to Rq, and γ0 is a q × 1 unknown parameter vector. When α0 � = 0, then dropout is nonignorable. On account of identifiability problems, joint estimation of the mean µ0 of Y and the selection bias parameter α0 may be difficult or impossible. Therefore, we propose regarding the selection bias parameter α0 as known, rather than estimating it from the data. We then perform a sensitivity analysis to see how inference about µ0 changes as we vary α0 over a plausible range of values. We apply our approach to the analysis of ACTG 175, an AIDS clinical trial. KEY WORDS: Augmented inverse probability of censoring weighted estimators; Cox proportional hazards model; Identification;
Consistent Testing for Stochastic Dominance under General Sampling Schemes” Review of Economic Studies
, 2005
"... We propose a procedure for estimating the critical values of the extended KolmogorovSmirnov tests of Stochastic Dominance of arbitrary order in the general Kprospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate general dependence amongst ..."
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Cited by 102 (15 self)
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We propose a procedure for estimating the critical values of the extended KolmogorovSmirnov tests of Stochastic Dominance of arbitrary order in the general Kprospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that the resulting tests are consistent and powerful against some N −1/2 local alternatives. We also propose some heuristic methods for selecting subsample size and demonstrate in simulations that they perform reasonably. We describe an alternative method for obtaining critical values based on recentering the test statistic and using full sample bootstrap methods. We compare the two methods in theory and in practice.
Linear smoothers and additive models
 The Annals of Statistics
, 1989
"... We study linear smoothers and their use in building nonparametric regression models. In part Qfthis paper we examine certain aspects of linear smoothers for scatterplots; examples of these are the running mean and running line, kernel, and cubic spline smoothers. The eigenvalue and singular value d ..."
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Cited by 101 (2 self)
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We study linear smoothers and their use in building nonparametric regression models. In part Qfthis paper we examine certain aspects of linear smoothers for scatterplots; examples of these are the running mean and running line, kernel, and cubic spline smoothers. The eigenvalue and singular value decompositions of the corresponding smoother matrix are used to qualitatively describe a smoother, and several other topics such as the number of degrees of freedom of a smoother are discussed. In the second part of the paper we describe how Iinearsmoothers can be used to estimate the additive model, a powerful nonparametric regression model, using the "backfitting algorithm". We study the convergence of the backfitting algorithm and prove its convergence for a class of smoothers that includes cubic e:ttJlCl€~nt jJI:::Jll<l.li:6I;:U least squares. algorithm and ' dis.cuss ev'W()r(is: Neaparametric, seanparametric, regression, GaussSeidelalgorithm,