Flexible quantile regression models: application to the study of the purple sea urchin

In many applications, it is often of interest to assess the possible relationships between covariates and quantiles of a response variable through a regression model. In some instances, the effects of continuous covariates on the outcome are highly nonlinear. Consequently, appropriate modelling has to take such flexible smooth effects into account. In this work, various flexible quantile regression techniques were reviewed and compared by simulation. Finally, all the techniques were used to construct the overall zone specific reference curves of morphologic measures of sea urchin Paracentrotus lividus (Lamarck, 1816) located in NW SpainPeer Reviewe

Flexible quantile regression models : application to the study of the purple sea urchin

In many applications, it is often of interest to assess the possible relationships between covariates and quantiles of a response variable through a regression model. In some instances, the effects of continuous covariates on the outcome are highly nonlinear. Consequently, appropriate modelling has to take such flexible smooth effects into account. In this work, various flexible quantile regression techniques were reviewed and compared by simulation. Finally, all the techniques were used to construct the overall zone specific reference curves of morphologic measures of sea urchin Paracentrotus lividus (Lamarck, 1816) located in NW Spain

Social constraints in cross-boundary collaborative deer management

This study was funded by the Norwegian Research Council as part of the DeerUnit project (Grant number 230275). We are very grateful to all landowners who took part in the survey. We would also like to acknowledge comments from Vebjørn Veiberg on a previous draft.Peer reviewedPublisher PD

Moving beyond green : Exploring the relationship of environment type and indicators of perceived environmental quality on emotional well-being following group walks

Against the backdrop of increasing interest in the relationship between Nature and health, this study examined the effect of perceived environment type and indicators of perceived environmental quality on short-term emotional well-being following outdoor group walks. Participants (n = 127) of a national group walk program completed pre- and post-walk questionnaires for each walk attended (n = 1009) within a 13-week study period. Multilevel linear modelling was used to examine the main and moderation effects. To isolate the environmental from the physical activity elements, analyses controlled for walk duration and perceived intensity. Analyses revealed that perceived restorativeness and perceived walk intensity predicted greater positive affect and happiness following an outdoor group walk. Perceived restorativeness and perceived bird biodiversity predicted post-walk negative affect. Perceived restorativeness moderated the relationship between perceived naturalness and positive affect. Results suggest that restorative quality of an environment may be an important element for enhancing well-being, and that perceived restorativeness and naturalness of an environment may interact to amplify positive affect. These findings highlight the importance of further research on the contribution of environment type and quality on well-being, and the need to control for effects of physical activity in green exercise research

Statistical methods for the analysis of ordinal response data

Ordinal response models and in particular cumulative link models are the most prevalent techniques for modelling ordered response data. This thesis examines the advantages of these models versus other approaches in the socio-economic literature and assesses the performance of available residual diagnostics measures by means of simulation studies and four case studies. Furthermore, it proposes solutions to specific issues of flexible versions of cumulative link models

Causal inference for circular economy data

A project funded by the Scottish Government's 2022-2027 Rural and Environment Science and Analytical Services Division (RESAS) Strategic Research Programme