Results 1  10
of
610
A new method for nonparametric multivariate analysis of variance in ecology.
 Austral Ecology,
, 2001
"... Abstract Hypothesistesting methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, ..."
Abstract

Cited by 368 (4 self)
 Add to MetaCart
, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description provided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous nonparametric
Learning attractor landscapes for learning motor primitives
 in Advances in Neural Information Processing Systems
, 2003
"... Many control problems take place in continuous stateaction spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control p ..."
Abstract

Cited by 195 (28 self)
 Add to MetaCart
, we represent canonical policies in terms of differential equations with welldefined attractor properties. By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability
Estimating the technology of cognitive and noncognitive skill formation. Manuscript
, 2006
"... This paper formulates and estimates multistage production functions for children’s cognitive and noncognitive skills. Skills are determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and stoc ..."
Abstract

Cited by 189 (43 self)
 Add to MetaCart
This paper formulates and estimates multistage production functions for children’s cognitive and noncognitive skills. Skills are determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period
Component selection and smoothing in multivariate nonparametric regression
"... We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The “COSSO ” is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in ..."
Abstract

Cited by 76 (1 self)
 Add to MetaCart
We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The “COSSO ” is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed
Bayesian Nonparametric Models
"... A Bayesian nonparametric model is a Bayesian model on an infinitedimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions, a ..."
Abstract

Cited by 17 (0 self)
 Add to MetaCart
complexity of the model (as measured by the number of dimensions used) adapts to the data. Classical adaptive problems, such as nonparametric estimation and model selection, can thus be formulated as Bayesian inference problems. Popular examples of Bayesian nonparametric models include Gaussian process
Nonparametric seismic data recovery with curvelet frames
 Geophysical Journal International
, 2008
"... Seismic data recovery from data with missing traces on otherwise regular acquisition grids forms a crucial step in the seismic processing flow. For instance, unsuccesful recovery leads to imaging artifacts and to erroneous predictions for the multiples, adversely affecting the performance of multipl ..."
Abstract

Cited by 56 (16 self)
 Add to MetaCart
of multiple ellimination. A nonparametric transformbased recovery method is presented that exploits the compression of seismic data volumes by multidimensional expansions with respect to recently developed curvelet frames. The frame elements of these transforms locally resemble wavefronts present
FORMULATION AND SOLUTION STRATEGIES FOR NONPARAMETRIC NONLINEAR STOCHASTIC PROGRAMS, WITH AN APPLICATION IN FINANCE
, 2007
"... nonparametric nonlinear stochastic programs, with an application in finance. ..."
Abstract
 Add to MetaCart
nonparametric nonlinear stochastic programs, with an application in finance.
Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces
 Journal of Machine Learning Research
, 2004
"... We propose a novel method of dimensionality reduction for supervised learning problems. Given a regression or classification problem in which we wish to predict a response variable Y from an explanatory variable X, we treat the problem of dimensionality reduction as that of finding a lowdimensional ..."
Abstract

Cited by 162 (34 self)
 Add to MetaCart
dimensional “effective subspace ” for X which retains the statistical relationship between X and Y. We show that this problem can be formulated in terms of conditional independence. To turn this formulation into an optimization problem we establish a general nonparametric characterization of conditional independence
Bayesian Nonparametric Covariance Regression
, 1101
"... Summary. Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression mode ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
Summary. Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression
Nonparametric Likelihood: Efficiency and Robustness
 Japanese Economic Review
, 2007
"... Abstract. Nonparametric likelihood is a natural generalization of the parametric maximum likelihood estimation (MLE) procedure, which has been the workhorse in empirical economics. An interesting fact is that the MLE procedure remains valid in many stochastic models that have nonparametric componen ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
nents, provided that the “likelihood function ” is formulated appropriately. Such a likelihood function can be obtained by using multinomial distribution functions, supported by observed data values, to approximate the underlying distribution nonparametrically. This yields the socalled nonparametric likelihood
Results 1  10
of
610