We consider testing whether the nonparametric function in a semiparametric additive mixed model is a simple fixed degree polynomial, for example, a simple linear function. This test provides a goodness-of-fit test for checking parametric models against nonparametric models. It is based on the mixed-model representation of the smoothing spline estimator of the nonparametric function and the vari...

Additive model is an effective dimension reduction model that provides flexibility to model the relation between a response variable and key covariates. The literature is largely developed to scalar response and vector covariates. In this paper, more complex data is of interest, where both the response and covariates may be functions. A functional additive model is proposed together with a new ...

We propose to learn generalized additive models for classification which represents the classifier using a sum of piecewise linear functions and show that a recently proposed fast linear SVM training method (Pegasos) can be adapted to train such models with the same convergence rates. To be able to learn functions on combination of dimensions, we explore the use of random projection features wh...

We compare several accounting based models for bankruptcy prediction. The models are developed and tested on large data sets containing annual financial statements for Norwegian limited liability firms. Out-of-sample and out-of-time validation shows that generalized additive models significantly outperform popular models like linear discriminant analysis, generalized linear models and neural ne...

We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an additive noise structure, the directed acyclic graph becomes identifiable fro...

In recent years, there has been considerable interest in estimating conditional independence graphs in high dimensions. Most previous work has assumed that the variables are multivariate Gaussian, or that the conditional means of the variables are linear; in fact, these two assumptions are nearly equivalent. Unfortunately, if these assumptions are violated, the resulting conditional independenc...

We develop a generalized additive modeling framework for taking into account the effect of predictors on the dependence structure between two variables. We consider dependence or concordance measures that are solely functions of the copula, because they contain no marginal information: rank correlation coefficients or tail-dependence coefficients represent natural choices. We propose a maximum ...

We study the behavior of the l1 type of regularization for high dimensional additive models. Our results suggest remarkable similarities and differences between linear regression and additive models in high dimensional settings. In particular, our analysis indicates that, unlike in linear regression, l1 regularization does not yield optimal estimation for additive models of high dimensionality....