Mixing compositions and other scales

Theory of compositional data analysis is often focused on the composition only. However in practical applications we often treat a composition together with covariables with some other scale. This contribution systematically gathers and develop statistical tools for this situation. For instance, for the graphical display of the dependence of a composition with a categorical variable, a colored set of ternary diagrams might be a good idea for a first look at the data, but it will fast hide important aspects if the composition has many parts, or it takes extreme values. On the other hand colored scatterplots of ilr components could not be very instructive for the analyst, if the conventional, black-box ilr is used. Thinking on terms of the Euclidean structure of the simplex, we suggest to set up appropriate projections, which on one side show the compositional geometry and on the other side are still comprehensible by a non-expert analyst, readable for all locations and scales of the data. This is e.g. done by defining special balance displays with carefully- selected axes. Following this idea, we need to systematically ask how to display, explore, describe, and test the relation to complementary or explanatory data of categorical, real, ratio or again compositional scales. This contribution shows that it is sufficient to use some basic concepts and very few advanced tools from multivariate statistics (principal covariances, multivariate linear models, trellis or parallel plots, etc.) to build appropriate procedures for all these combinations of scales. This has some fundamental implications in their software implementation, and how might they be taught to analysts not already experts in multivariate analysis

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Multivariate Statistical Process Control and Case-Based Reasoning for situation assessment of Sequencing Batch Reactors

ABSRACT This thesis focuses on the monitoring, fault detection and diagnosis of Wastewater Treatment Plants (WWTP), which are important fields of research for a wide range of engineering disciplines. The main objective is to evaluate and apply a novel artificial intelligent methodology based on situation assessment for monitoring and diagnosis of Sequencing Batch Reactor (SBR) operation. To this end, Multivariate Statistical Process Control (MSPC) in combination with Case-Based Reasoning (CBR) methodology was developed, which was evaluated on three different SBR (pilot and lab-scales) plants and validated on BSM1 plant layout.

Primary succession in man-made wetlands: biodiversity, structure and dynamics of macrofaunal assemblages

Man-made wetlands are often created to compensate for the loss or degradation of natural wetlands, but little is known about the processes taking place in these artificial environments, especially at the community level. Throughout this thesis, we have assessed the phenomena of primary succession over different time (short-, mid- and long-term) and spatial scales (local, regional, interregional levels), applying different approaches (taxonomic and functional) and subject groups (invertebrates and amphibians). Our main findings regarding time scales show a 3-phase successional pattern in Mediterranean man-made wetlands’ communities, where at the short term (1 year) colonization processes dominate; at mid term perspectives (2 to 7 years) succession signs begin to be conspicuous, and later on (≥ 10 years) parameters such as species richness reach an asymptote. At that moment, some biological strategies dominate, and biodiversity surrogates indicate that communities are indistinct between man-made and natural wetlands. Regarding spatial effects, we corroborated that both local and regional factors affect the establishing communities. Particularly, the low hydrological stability of the Mediterranean region has enhanced biological traits favoring resilience and resistance to disturbances when comparing Mediterranean and cold temperate aquatic communities. Even within the Mediterranean region, low levels of hydrological stability have significant effects on the successional dynamics. In these cases, local communities are highly nested within regional natural ones, and so are not able to make net contributions to regional richness. We also showed the influence of the regional pool of recruiters over local communities, both in the case of invertebrates and amphibians. Especially for the latter group, man-made Mediterranean temporary ponds (MTPs) can play an important role in their conservation.