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Additive Predictor (additive + predictor)

Distribution by Scientific Domains

Psychology	50%
Mathematics and Statistics	50%

Selected Abstracts

A spatial model for the needle losses of pine-trees in the forests of Baden-Württemberg: an application of Bayesian structured additive regression

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2007
Nicole H. Augustin
Summary., The data that are analysed are from a monitoring survey which was carried out in 1994 in the forests of Baden-Württemberg, a federal state in the south-western region of Germany. The survey is part of a large monitoring scheme that has been carried out since the 1980s at different spatial and temporal resolutions to observe the increase in forest damage. One indicator for tree vitality is tree defoliation, which is mainly caused by intrinsic factors, age and stand conditions, but also by biotic (e.g. insects) and abiotic stresses (e.g. industrial emissions). In the survey, needle loss of pine-trees and many potential covariates are recorded at about 580 grid points of a 4 km × 4 km grid. The aim is to identify a set of predictors for needle loss and to investigate the relationships between the needle loss and the predictors. The response variable needle loss is recorded as a percentage in 5% steps estimated by eye using binoculars and categorized into healthy trees (10% or less), intermediate trees (10,25%) and damaged trees (25% or more). We use a Bayesian cumulative threshold model with non-linear functions of continuous variables and a random effect for spatial heterogeneity. For both the non-linear functions and the spatial random effect we use Bayesian versions of P -splines as priors. Our method is novel in that it deals with several non-standard data requirements: the ordinal response variable (the categorized version of needle loss), non-linear effects of covariates, spatial heterogeneity and prediction with missing covariates. The model is a special case of models with a geoadditive or more generally structured additive predictor. Inference can be based on Markov chain Monte Carlo techniques or mixed model technology. [source]

Peer Rejection, Aggressive or Withdrawn Behavior, and Psychological Maladjustment from Ages 5 to 12: An Examination of Four Predictive Models

CHILD DEVELOPMENT, Issue 4 2006
Gary W. Ladd
Findings yielded a comprehensive portrait of the predictive relations among children's aggressive or withdrawn behaviors, peer rejection, and psychological maladjustment across the 5,12 age period. Examination of peer rejection in different variable contexts and across repeated intervals throughout childhood revealed differences in the timing, strength, and consistency of this risk factor as a distinct (additive) predictor of externalizing versus internalizing problems. In conjunction with aggressive behavior, peer rejection proved to be a stronger additive predictor of externalizing problems during early rather than later childhood. Relative to withdrawn behavior, rejection's efficacy as a distinct predictor of internalizing problems was significant early in childhood and increased progressively thereafter. These additive path models fit the data better than did disorder-driven or transactional models. [source]

Structured additive regression for overdispersed and zero-inflated count data

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2006
Ludwig Fahrmeir
Abstract In count data regression there can be several problems that prevent the use of the standard Poisson log-linear model: overdispersion, caused by unobserved heterogeneity or correlation, excess of zeros, non-linear effects of continuous covariates or of time scales, and spatial effects. We develop Bayesian count data models that can deal with these issues simultaneously and within a unified inferential approach. Models for overdispersed or zero-inflated data are combined with semiparametrically structured additive predictors, resulting in a rich class of count data regression models. Inference is fully Bayesian and is carried out by computationally efficient MCMC techniques. Simulation studies investigate performance, in particular how well different model components can be identified. Applications to patent data and to data from a car insurance illustrate the potential and, to some extent, limitations of our approach. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Perceiving communion in the dyadic relationship of others

ASIAN JOURNAL OF SOCIAL PSYCHOLOGY, Issue 2 2006
Sylvia Xiaohua Chen
The present research took an ecological approach to explore a relational issue in social perception , are the perceived personality characteristics of dyad members and their perceived behavioural exchanges related to perceptions of that dyad's perceived level of friendship? To this end, observers reported on a dyad they knew well using an indigenous measure of personality perception and the Dyadic Behavioural Exchange Scale, combined with an adapted version of Hays' Friendship Observation Checklist. Perceived similarity of personality on the dimensions of application, emotional stability (negative) and helpfulness (negative) along with the perceived level of dyadic behaviour exchange were found to be additive predictors of perceived communion or friendship strength. This interplay of personal and interpersonal processes has demonstrated the application of methodological relationalism in the social domain, and broadened the ambit of social cognition to include knowledge of relationship units of which the observer is not a member, but which plays a part in his or her social world. [source]