It is well known that using individual covariate information (such as body weight or gender) to model heterogeneity in capture–recapture (CR) experiments can greatly enhance inferences on the size of a closed population. Since individual covariates are only observable for captured individuals, complex conditional likelihood methods are usually required and these do not constitute a standard gen...

In this article, we develop and investigate a new classifier based on features extracted using spatial depth. Our construction is based on fitting a generalized additive model to posterior probabilities of different competing classes. To cope with possible multi-modal as well as non-elliptic nature of the population distribution, we also develop a localized version of spatial depth and use that...

Identification of a nonlinear additive system is considered. An input signal is designed in such a way that the problem of identification of nonlinear additive systems is reduced to a problem of identification of static nonlinear functions. Then, three approaches are established to estimate the order of the system. The methods exploit the structure of the nonlinear additive model so that their ...

The lme4 package uses sparse matrix technology and clever decompositions of the likelihood to fit linear, generalized, and nonlinear mixed-effects models. The amer package extends lme4’s scope to include generalized additive mixed models (GAMM). This vignette summarizes the main ideas behind additive models and their representation in the form of a mixed model, describes the modifications to lm...

We explore additive models that combine both parametric and nonparamet-ric terms and propose a p n-consistent backktting estimator for the parametric component of the model. The theoretical properties of the estimator are developed for the case with a single nonparametric term and extended to an arbitrary number of nonparametric additive terms. An estimator for the optimal band-width making min...

The perception of prosodic prominence is influenced by different sources like different acoustic cues, linguistic expectations and context. We use a generalized additive model and a random forest to model the perceived prominence on a corpus of spoken German. Both models are able to explain over 80% of the variance. While the random forests give us some insights on the relative importance of th...

We develop a non-parametric cost function using generalized additive models and demonstrate how to test for input separability. Our empirical example focuses on Canadian cable television (CATV) provision. We estimate a new non-parametric cost function for this industry using financial and operating data collected between 1990 and 1996. This period is of particular importance from a policy persp...

Single-site recreation demand and dichotomous choice contingent valuation analyses are typically conducted by implementing models containing strong parametric assumptions, which are rarely underpinned by theoretical arguments. This work illustrates how these assumptions can be relaxed and the estimation conducted semiparametrically by using generalized additive models (GAMs). This approach dire...