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- Helen Hao, Zhang, Grace Wahba, Yi Lin, Meta Voelker, Michael Ferris +2 others
- 2003

- Weiliang Shi, Grace Wahba, Stephen Wright, Kristine Lee, Ronald Klein, Barbara Klein
- 2008

The LASSO-Patternsearch algorithm is proposed to efficiently identify patterns of multiple dichotomous risk factors for outcomes of interest in demographic and genomic studies. The patterns considered are those that arise naturally from the log linear expansion of the multivariate Bernoulli density. The method is designed for the case where there is a… (More)

- Helen Hao, Grace Zhang, Yi Wahba, Meta Lin, Michael Voelker, Ronald Ferris +2 others
- 2004

This paper presents a nonparametric penalized likelihood approach for variable selection and model building, called likelihood basis pursuit (LBP). In the setting of a tensor product reproducing kernel Hilbert space, we decompose the log likelihood into the sum of different functional components such as main effects and interactions, with each component… (More)

We combine a smoothing spline analysis of variance (SS-ANOVA) model and a log-linear model to build a partly exible model for multivariate Bernoulli data. The joint distribution conditioning on the predictor variables is estimated. The log odds ratio is used to measure the association between outcome variables. A numerical scheme based on the block one-step… (More)

We propose the randomized Generalized Approximate Cross Validation (ranGACV) method for choosing multiple smoothing parameters in penalized likelihood estimates for Bernoulli data. The method is intended for application with penalized likelihood smoothing spline ANOVA models. In addition we propose a class of approximate numerical methods for solving the… (More)

We propose a new in-sample cross validation based method (randomized GACV) for choosing smoothing or bandwidth parameters that govern the bias-variance or fit-complexity tradeoff in 'soft' classification. Soft classification refers to a learning procedure which estimates the probability that an example with a given attribute vector is in class 1 vs class O.… (More)

- Weiliang Shi, Grace Wahba, Stephen Wright, Kristine Lee, Ronald Klein, Barbara Klein
- 2006

The LASSO-Patternsearch algorithm is proposed to efficiently identify patterns of multiple dichotomous risk factors for outcomes of interest in demographic and genomic studies. The patterns considered are those that arise naturally from the log linear expansion of the multivariate Bernoulli density. The method is designed for the case where there is a… (More)

- Hao Zhang, Grace Wahba, Yi Lin, Meta Voelker, Michael Ferris, Ronald Klein +1 other
- 2001

- Weiliang Shi, Grace Wahba, Stephen Wright, Kristine Lee, Ronald Klein, Barbara Klein
- 2006

The LASSO-Patternsearch is proposed, as a two-stage procedure to identify clusters of multiple risk factors for outcomes of interest in large demographic studies, when the predictor variables are dichotomous or take on values in a small finite set. Many diseases are suspected of having multiple interacting risk factors acting in concert, and it is of much… (More)

We describe the use of smoothing spline analysis of variance (SS-ANOVA) in the penalized log likelihood context, for learning (estimating) the probability p of a '1' outcome, given a training set with attribute vectors and outcomes. p is of the form pet) = eJ(t) /(1 + eJ(t)), where, if t is a vector of attributes, f is learned as a sum of smooth functions… (More)