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Conditional Models (conditional + models)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

Socio-demographic risk factors for alcohol and drug dependence: the 10-year follow-up of the national comorbidity survey

ADDICTION, Issue 8 2009
Joel Swendsen
ABSTRACT Aims Continued progress in etiological research and prevention science requires more precise information concerning the specific stages at which socio-demographic variables are implicated most strongly in transition from initial substance use to dependence. The present study examines prospective associations between socio-demographic variables and the subsequent onset of alcohol and drug dependence using data from the National Comorbidity Survey (NCS) and the NCS Follow-up survey (NCS-2). Design The NCS was a nationally representative survey of the prevalence and correlates of DSM-III-R mental and substance disorders in the United States carried out in 1990,2002. The NCS-2 re-interviewed a probability subsample of NCS respondents a decade after the baseline survey. Baseline NCS socio-demographic characteristics and substance use history were examined as predictors of the first onset of DSM-IV alcohol and drug dependence in the NCS-2. Participants A total of 5001 NCS respondents were re-interviewed in the NCS-2 (87.6% of baseline sample). Findings Aggregate analyses demonstrated significant associations between some baseline socio-demographic variables (young age, low education, non-white ethnicity, occupational status) but not others (sex, number of children, residential area) and the subsequent onset of DSM-IV alcohol or drug dependence. However, conditional models showed that these risk factors were limited to specific stages of baseline use. Moreover, many socio-demographic variables that were not significant in the aggregate analyses were significant predictors of dependence when examined by stage of use. Conclusions The findings underscore the potential for socio-demographic risk factors to have highly specific associations with different stages of the substance use trajectory. [source]

Conditioning Information and European Bond Fund Performance

EUROPEAN FINANCIAL MANAGEMENT, Issue 2 2003
Florinda Silva
G11; G12; G14 In this paper we evaluate the performance of European bond funds using unconditional and conditional models. As conditioning information we use variables that we find to be useful in predicting bond returns in the European market. The results show that, in general, bond funds are not able to outperform passive strategies. These findings are robust to whatever model (unconditional versus conditional and single versus multi-index) we use. The multi-index model seems to add some explanatory power in relation to the single-index model. Furthermore, when we incorporate the predetermined information variables, we can observe a slight tendency towards better performance. This evidence is consistent with previous studies on stock funds and comes in support of the argument that conditional models might allow for a better assessment of performance. However, our results suggest that the impact of additional risk factors seems to be greater than the impact of incorporating predetermined information variables. [source]

Hierarchical Models in Environmental Science

INTERNATIONAL STATISTICAL REVIEW, Issue 2 2003
Christopher K. Wikle
Summary Environmental systems are complicated. They include very intricate spatio-temporal processes, interacting on a wide variety of scales. There is increasingly vast amounts of data for such processes from geographical information systems, remote sensing platforms, monitoring networks, and computer models. In addition, often there is a great variety of scientific knowledge available for such systems, from partial differential equations based on first principles to panel surveys. It is argued that it is not generally adequate to consider such processes from a joint perspective. Instead, the processes often must be considered as a coherently linked system of conditional models. This paper provides a brief overview of hierarchical approaches applied to environmental processes. The key elements of such models can be considered in three general stages, the data stage, process stage, and parameter stage. In each stage, complicated dependence structure is mitigated by conditioning. For example, the data stage can incorporate measurement errors as well as multiple datasets with varying supports. The process and parameter stages can allow spatial and spatio-temporal processes as well as the direct inclusion of scientific knowledge. The paper concludes with a discussion of some outstanding problems in hierarchical modelling of environmental systems, including the need for new collaboration approaches. Résumé Les systèmes environnementaux sont complexes. Ils incluent des processus spatio-temporels trés complexes, interagissant sur une large variété d'échelles. II existe des quantités de plus en plus grandes de données sur de tels processus, provenant des systèmes d'information géographiques, des plateformes de télédétection, des réseaux de surveillance et des modèles informatiques. De plus, il y a souvent une grande variété de connaissance scientifique disponible sur de tels systémes, depuis les équations différentielles partielles jusqu'aux enquétes de panels. II est reconnu qu'il n'est généralement pas correct de considerer de tels processus d'une perspective commune. Au contraire, les processus doivent souvent étre examinés comme des systèmes de modèles conditionnels liés de manière cohérente. Cet article fournit un bref aperçu des approches hiérachiques appliquées aux processus environnementaux. Les éléments clés de tels modèles peuvent étre examinés à trois étapes principales: l'étape des donnèes, celle du traitement et celle des paramètres. A chaque étape, la structure complexe de dépendance est atténuée par le conditionnement. Par exemple, le stade des données peut incorporer des erreurs de mesure ainsi que de multiples ensembles de données sous divers supports. Les stades du traitement et des paramétres peuvent admettre des processus spatiaux et spatio-temporels ainsi que l'inclusion directe du savoir scientifique. L'article conclut par une discussion de quelques problèmes en suspens dans la modélisation hiérarchique des systèmes environnementaux, incluant le besoin de nouvelles approches de collaboration. [source]

Likelihood analysis of joint marginal and conditional models for longitudinal categorical data

THE CANADIAN JOURNAL OF STATISTICS, Issue 2 2009
Baojiang Chen
MSC 2000: Primary 62H12; secondary 62F10 Abstract The authors develop a Markov model for the analysis of longitudinal categorical data which facilitates modelling both marginal and conditional structures. A likelihood formulation is employed for inference, so the resulting estimators enjoy the optimal properties such as efficiency and consistency, and remain consistent when data are missing at random. Simulation studies demonstrate that the proposed method performs well under a variety of situations. Application to data from a smoking prevention study illustrates the utility of the model and interpretation of covariate effects. The Canadian Journal of Statistics © 2009 Statistical Society of Canada Les auteurs développent un modèle de Markov pour l'analyse de données catégorielles longitudinales facilitant la représentation des structures marginales et conditionnelles. L'inférence est basée sur une fonction de vraisemblance afin d'obtenir des estimateurs efficaces, cohérents et qui le demeurent lorsqu'il y a des données manquantes au hasard. Des études de simulation montrent que la méthode proposée se comporte bien dans les différents scénarios considérés. L'application à des données provenant d'une étude sur la lutte contre le tabagisme illustre bien l'utilité de ce modèle et permet une interprétation des effets des covariables. La revue canadienne de statistique © 2009 Société statistique du Canada [source]