Subjectivism as an unavoidable feature of ecological statistics

© 2014 Museu de Ciències Naturals de Barcelona. Subjectivism as an unavoidable feature of ecological statistics.— We approach here the handling of previous information when performing statistical inference in ecology, both when dealing with model specification and selection, and when dealing with parameter estimation. We compare the perspectives of this problem from the frequentist and Bayesian schools, including objective and subjective Bayesians. We show that the issue of making use of previous information and making a priori decisions is not only a reality for Bayesians but also for frequentists. However, the latter tend to overlook this because of the common difficulty of having previous information available on the magnitude of the effect that is thought to be biologically relevant. This prior information should be fed into a priori power tests when looking for the necessary sample sizes to couple statistical and biological significances. Ecologists should make a greater effort to make use of available prior information because this is their most legitimate contribution to the inferential process. Parameter estimation and model selection would benefit if this was done, allowing a more reliable accumulation of knowledge, and hence progress, in the biological sciences.Peer Reviewe

Multivariate regression smoothing through the 'fallling net'.

We consider multivariate regression smoothing through a conditional mean shift procedure. By computing local conditional means iteratively over a set or grid of target points, at each iteration a `net' is formed which gently drifts towards the data cloud, until it settles at the conditional modes of the response distribution. The method is edge-preserving and allows for multi-valued response

Exploring Bayesian models to evaluate control procedures for plant disease

Tigernut tubers are the main ingredient in the production of orxata in Valencia, a white soft sweet popular drink. In recent years, the appearance of black spots in the skin of tigernuts has led to important economic losses in orxata production because severely diseased tubers must be discarded. In this paper, we discuss three complementary statistical models to assess the disease incidence of harvested tubers from selected or treated seeds, and propose a measure of effectiveness for different treatments against the disease based on the probability of germination and the incidence of the disease. Statistical methods for these studies are approached from Bayesian reasoning and include mixed-effects models, Dirichlet-multinomial inferential processes and mixed-effects logistic regression models. Statistical analyses provide relevant information to carry out measures to palliate the black spot disease and achieve a high-quality production. For instance, the study shows that avoiding affected seeds increases the probability of harvesting asymptomatic tubers. It is also revealed that the best chemical treatment, when prioritizing germination, is disinfection with hydrochloric acid while sodium hypochlorite performs better if the priority is to have a reduced disease incidence. The reduction of the incidence of the black spots syndrome by disinfection with chemical agents supports the hypothesis that the causal agent is a pathogenic organism

Urban spatial structure and economic growth in Spanish metropolitan areas

There is a large literature on the existence of agglomeration economies, as shown in the surveys by Moomaw (1983) or Gerking (1993). The benefits of these economies arise from multiple sources, but some negative externalities might also emerge. Within the hierarchical urban system, cities at different ranks (different size) take on different economic functions with variant 'efficient sizes' (Capello and Camagni, 2000) and, indeed, the distributions of cities' relative size have been stable in many countries (Black and Henderson, 1999; Eaton and Eckstein, 1997; Nitsch, 2005) and, in many cases they obey the Zipf's law (Gabaix, 1999). If a city is able to adjust its spatial structure to offset the negative exter- nalities due to its size, it will be able to keep growing. If that is not possible, it might be more convenient to transit from a monocentric to a polycentric structure, which is usually considered as a possible strategy to eliminate diseconomies in urban economics (Sasaki and Mun, 1996; Fujita et al., 1997). However, there is little empirical evidence on the links between urban spatial structure and growth---which are usually understood within the context of urban evolution. One notable exception is the study by Cervero (2001), where it is argued that more compact, centralized and accessible cities are usually associated with higher productivity levels. In this context, this paper explores the links between urban spatial structure and economic growth in metropolitan areas in Spain, where this type of analysis is virtually non-existent. However, it is a relevant policy issue due to a variety of reasons such as the increased urban sprawl and the different costs it brings about. The analysis will also enable to evaluate if there is any particular type of urban spatial structure which prevails on the grounds of its superior efficiency, together with evaluating if an efficient urban spatial structure hinges on the size and other attributes specific to each particular metropolitan area

Variable selection in the analysis of energy consumption-growth nexus

There is abundant empirical literature that focuses on whether energy consumption is a critical driver of economic growth. The evolution of this literature has largely consisted of attempts to solve the problems and answer the criticisms arising from earlier studies. One of the most common criticisms is that previous work concentrates on the bivariate relationship, energy consumption–economic growth. Many authors try to overcome this critique using control variables. However, the choice of these variables has been ad hoc, made according to the subjective economic rationale of the authors. Our contribution to this literature is to apply a robust probabilistic model to select the explanatory variables from a large set of potential candidates in the case of the US from 1949 to 2010, not only for an aggregate analysis but also for a sector analysis. The results highlight the critical role of public spending and energy intensity in the explanation of growth. Furthermore, since the study reveals different explanatory variables for each sector, it indicates the importance of policy decisions specifically aimed at different sectors.Generalitat Valenciana project PROMETEOII/2014/053, MINECO project ECO2014-58991-C3-2-R UJI project P1-1B2014-17 Spanish Ministry of Economy and Competitiveness project: MTM2013- 42323-

Bayesian Testing, Variable Selection and Model Averaging in Linear Models using R with BayesVarSel

In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportional-hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event-free probabilities and predict future longitudinal marker values. The model is applied to the assessment of breast cancer risk in women attending a population-based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI-RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.Peer ReviewedPostprint (author's final draft

Short and Long-Term Trainability in Older Adults: Training and Detraining Following Two Years of Multicomponent Cognitive-Physical Exercise Training

Despite the benefits of multicomponent physical–cognitive training programs (MCCogTPs), lower training intensities in the concurrent approach, and bigger heterogeneity with aging, suggest the need for long-term analyses, with special attention to training and detraining in older adults. The present study aims to examine these training/detraining effects in a two year MCCogTP, looking for specific dynamics in the trainability of their physical and cognitive capacities. The intervention was divided into four periods: T1, T2 (8 months of training each), and D1, D2 (3.5 months of detraining plus 0.5 of testing each). Twenty-five healthy seniors (70.82 ± 5.18 years) comprised the final sample and were assessed for cardiovascular fitness (6-minutes walking test), lower-limbs strength (30-seconds chair-stand test) and agility (8-feet timed up-and-go test). Inhibition (Stroop test) was considered for executive function. Physical and cognitive status improved significantly (p < 0.05) throughout the two years, with larger enhancements for physical function (mainly strength and agility). Strength and cardiovascular fitness were more sensitive to detraining, whilst agility proved to have larger training retentions. Inhibition followed an initial similar trend, but it was the only variable to improve along D2 (d = 0.52), and changes were not significant within periods. Notwithstanding aging, and the exercise cessation in D2, physical and cognitive status remained enhanced two years later compared to baseline, except for lower-limb strength. According to these results, basic physical capacities are very sensitive to training/detraining, deserving continuous attention (especially strength). Both reducing detraining periods and complementary resistance training should be considered. Additionally, physical enhancements following MCcogTPs may help cognition maintenance during detraining

Multivariate regression smoothing through the 'fallling net'

We consider multivariate regression smoothing through a conditional mean shift procedure. By computing local conditional means iteratively over a set or grid of target points, at each iteration a `net' is formed which gently drifts towards the data cloud, until it settles at the conditional modes of the response distribution. The method is edge-preserving and allows for multi-valued response