The “No Miracle Argument” for scientific realism contends that the only plausible explanation for the predictive success of scientific theories is their truthlikeness, but doesn’t specify what ‘truthlikeness’ means. I argue that if we understand ‘truthlikeness’ in terms of Kullback-Leibler (KL) divergence, the resulting realist thesis (RKL) is a plausible explanation for science’s success. Still, RKL probably falls short of the realist’s ideal. I argue, however, that the strongest version of realism that the argument can plausibly establish is RKL. The realist needs another argument for establishing a stronger realist thesis.

We aimed to identify temporal patterns of energy intake and investigate their association with adiposity. We performed a cross-sectional study of 775 adults in Iran. Information about eating occasions across the day was collected by three 24-h dietary recalls. Latent class analysis (LCA) was used to identify temporal eating patterns based on whether or not an eating occasion occurred within each hour of the day. We applied binary logistic regression to estimate the OR and 95 % CI of overweight and obesity (defined as BMI of 25–29·9 and ≥ 30 kg/m2, respectively) across temporal eating patterns while controlling for potential confounders. LCA grouped participants into three exclusive sub-groups named ‘Conventional’, ‘Earlier breakfast’ and ‘Later lunch’. The ‘Conventional’ class was characterised by high probability of eating occasions at conventional meal times. ‘Earlier breakfast’ class was characterised by high probability of a breakfast eating occasion 1 h before the conventional pattern and a dinner eating occasion 1 h after the conventional pattern, and the ‘Later lunch’ class was characterised by a high probability of a lunch eating occasion 1 h after the conventional pattern. Participants in the ‘Earlier breakfast’ pattern had a lower likelihood of obesity (adjusted OR: 0·56, 95 % CI: 0·35, 0·95) as compared with the ‘Conventional’ pattern. There was no difference in the prevalence of obesity or overweight between participants in the ‘Later lunch’ and the ‘Conventional’ patterns. We found an inverse association between earlier eating pattern and the likelihood of obesity, but reverse causation may be a plausible explanation.

Depression is a chronic and complex condition experienced by over 300 million people worldwide. While research on the impact of nutrition on chronic physical illness is well documented, there is growing interest in the role of dietary patterns for those experiencing symptoms of depression. This study aims to examine the association of diet quality (Dietary Questionnaire for Epidemiological Studies version 2) and depressive symptoms (Centre for Epidemiological Studies for Depression short form) of young Australian women over 6 years at two time points, 2003 (n 9081, Mean age = 27·6) and 2009 (n 8199, Mean age = 33·7) using secondary data from the Australian Longitudinal Study on Women’s Health. A linear mixed-effects model found a small and significant inverse association of diet quality on depressive symptoms (β = −0·03, 95 % CI (−0·04, −0·02)) after adjusting for covarying factors such as BMI, social functioning, alcohol and smoking status. These findings suggest that the continuation of a healthy dietary pattern may be protective of depressive symptoms. Caution should be applied in interpreting these findings due to the small effect sizes. More longitudinal studies are needed to assess temporal relationships between dietary quality and depression.

The presence and density of two sympatric, large-sized vipers (the Gaboon viper, Bitis gabonica and the nose-horned viper, Bitis nasicornis) were studied along several transects, during both dry and wet seasons, and at different times of day, in southern Nigeria (West Africa). Three habitat types were found along the various transects (mature rain forest (MF), secondary rain forest (SF), swamp forest (SW)). The detection probabilities for these vipers were modelled with a set of competing models, and the various models were ordered by Akaike Information Criterion procedures. Two classes of models were used: the single-season model, and the multi-species model. The best models (single-season model) suggested that: for the Gaboon viper, habitat types SF and SW were particularly important in detecting this species, especially during the rainy season at 08h00–16h00. For nose-horned vipers, the best models had SW and MF as site-covariates. Application of the multi-species model revealed that there were different detection functions if both species are present at a site, with a ‘negative’ interaction of occupancy between the species. Females and males were similarly detectable in a logistic regression model, but feeding status and pregnancy slightly increased detection probability in a logistic regression model. Viper density was modelled by a DISTANCE sampling procedure. The density of one species tended to be inversely correlated to the density of the other, suggesting that (1) the rain-forest environment does not support abundant populations of both vipers when sympatric, and (2) the two Bitis species subtly partition the habitat resources.

This paper investigates the potential for nonlinear Granger causality from money to output. Using a standard four-variable linear (subset) vector error-correction model (VECM), we first show that the null hypothesis of linearity can be rejected against the alternative of smooth-transition autoregressive nonlinearity. An interesting result from this stage of the analysis is that the yearly growth rate of money is identified as one of the variables that may govern the switching between regimes. Smooth-transition VECM's (STVECM's) are then used to examine whether there is nonlinear Granger causality in the money–output relationship in the sense that lagged values of money enter the model's output equation as regressors. We evaluate this type of nonlinear Granger causality with both in-sample and out-of-sample analyses. For the in-sample analysis, we compare alternative models using the Akaike information criteria, which can be interpreted as a predictive accuracy test. The results show that allowing for both nonlinearity and for money–output causality leads to considerable improvement in model's in-sample performance. By contrast, the out-of-sample forecasting results do not suggest that money is nonlinearly Granger causal for output. They also show that, according to several criteria, the linear VECM's dominate the STVECM's. However, these forecast improvements seldomly are statistically significant at conventional levels.

The relationship between consistency of model selection and that of parameter estimation is investigated. It is shown that the consistency of model selection is achieved at the cost of a lower order of consistency of the resulting estimate of parameters in some domain. The situation is different when selecting autoregressive moving average models, since the information matrix becomes singular when overfitted. Some detailed analyses of the consistency are given in this case.

It is demonstrated that linear parametrization of the conditional intensity provides systematic classes of flexible models which are reasonably useful for calculating maximum likelihoods. To exemplify the modelling, seismic activity around Canberra is decomposed into components of evolutionary trend, clustering and periodicity. The causal relationship between earthquake sequences from two seismic regions is also analysed for a certain Japanese earthquake data set.

Some technical aspects of the modelling and calculations are described.