User profiles for G. M. James
Gareth JamesDean of Goizueta Business School, Emory Verified email at emory.edu Cited by 30410 |
Finding the number of clusters in a dataset: An information-theoretic approach
One of the most difficult problems in cluster analysis is identifying the number of groups in a
dataset. Most previously suggested approaches to this problem are either somewhat ad hoc …
dataset. Most previously suggested approaches to this problem are either somewhat ad hoc …
Clustering for sparsely sampled functional data
… been used previously in a functional classification setting (James and Hastie 2001). In the fi
nite… We use an alternative approach suggested by Sugar and James (2003) based on the “dis…
nite… We use an alternative approach suggested by Sugar and James (2003) based on the “dis…
Principal component models for sparse functional data
The elements of a multivariate dataset are often curves rather than single points. Functional
principal components can be used to describe the modes of variation of such curves. If one …
principal components can be used to describe the modes of variation of such curves. If one …
Generalized linear models with functional predictors
GM James - Journal of the Royal Statistical Society Series B …, 2002 - academic.oup.com
We present a technique for extending generalized linear models to the situation where
some of the predictor variables are observations from a curve or function. The technique is …
some of the predictor variables are observations from a curve or function. The technique is …
Functional linear discriminant analysis for irregularly sampled curves
We introduce a technique for extending the classical method of linear discriminant analysis (LDA)
to data sets where the predictor variables are curves or functions. This procedure, …
to data sets where the predictor variables are curves or functions. This procedure, …
Functional linear regression that's interpretable
Regression models to relate a scalar Y to a functional predictor X(t) are becoming increasingly
common. Work in this area has concentrated on estimating a coefficient function, β(t), with …
common. Work in this area has concentrated on estimating a coefficient function, β(t), with …
Variance and bias for general loss functions
GM James - Machine learning, 2003 - Springer
When using squared error loss, bias and variance and their decomposition of prediction error
are well understood and widely used concepts. However, there is no universally accepted …
are well understood and widely used concepts. However, there is no universally accepted …
DASSO: connections between the Dantzig selector and lasso
We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig
selector with a similar computational cost to the least angle regression algorithm that is used to …
selector with a similar computational cost to the least angle regression algorithm that is used to …
Functional additive regression
… James and Silverman [23] proposed an index model to implement a nonlinear functional
regression, and, more recently, both [14] and [7] extended this work to a fully nonparametric …
regression, and, more recently, both [14] and [7] extended this work to a fully nonparametric …
Functional regression: A new model for predicting market penetration of new products
… We use the “jump” approach (Sugar and James 2003) to select the optimal number of …
Sugar and James (2003) show through the use of information theory and simulations that setting …
Sugar and James (2003) show through the use of information theory and simulations that setting …