Classic and Bayesian Tree-Based Methods

Tree-based methods are nonparametric techniques and machine-learning methods for data prediction and exploratory modeling. These models are one of valuable and powerful tools among data mining methods and can be used for predicting different types of outcome (dependent) variable: (e.g., quantitative, qualitative, and time until an event occurs (survival data)). Tree model is called classification tree/regression tree/survival tree based on the type of outcome variable. These methods have some advantages over against traditional statistical methods such as generalized linear models (GLMs), discriminant analysis, and survival analysis. Some of these advantages are: without requiring to determine assumptions about the functional form between outcome variable and predictor (independent) variables, invariant to monotone transformations of predictor variables, useful for dealing with nonlinear relationships and high-order interactions, deal with different types of predictor variable, ease of interpretation and understanding results without requiring to have statistical experience, robust to missing values, outliers, and multicollinearity. Several classic and Bayesian tree algorithms are proposed for classification and regression trees, and in this chapter, we provide a review of these algorithms and appropriate criteria for determining the predictive performance of them

Alternative approaches to modelling lake ecosystems

The development of modelling in aquatic ecology has focused on mechanistic biogeochemical models.  However, such models have substantial data requirements for inputs and also for proper validation, which hinders their use for less studied systems.  Another significant problem with complex models is their structural and computational difficulty, and thus often an associated absence of proper uncertainty analysis for the model results.  This makes the use of the outputs for public policy making (e.g. in lake management) rather questionable.  We see no compelling reason (other than lack of awareness of choices) why all lakes and all questions should necessarily be studied using the same high-profile models.  Here we review two alternative statistical approaches, Linear Mixed Modelling and Structural Equation Modelling, and the different ways they have been used to extract maximum information from existing data.  These methods offer promise for tackling the problems highlighted above, although our aim is not to promote any one method over the others.  Rather, we want to stimulate debate about the remaining unknown factors in lake modelling as well as about the balance between data and models, and the still too uncritical way in which model outputs are interpreted and used for decision making