An optimal transportation approach for assessing almost stochastic order

When stochastic dominance $F\leq_{st}G$ does not hold, we can improve agreement to stochastic order by suitably trimming both distributions. In this work we consider the $L_2-$Wasserstein distance, $\mathcal W_2$, to stochastic order of these trimmed versions. Our characterization for that distance naturally leads to consider a $\mathcal W_2$-based index of disagreement with stochastic order, $\varepsilon_{\mathcal W_2}(F,G)$. We provide asymptotic results allowing to test $H_0: \varepsilon_{\mathcal W_2}(F,G)\geq \varepsilon_0$ vs $H_a: \varepsilon_{\mathcal W_2}(F,G)<\varepsilon_0$, that, under rejection, would give statistical guarantee of almost stochastic dominance. We include a simulation study showing a good performance of the index under the normal model

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method

BackgroundBiochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need for numerical methods that are both fast and accurate. The Bulirsch-Stoer method is an established method for solving ordinary differential equations that possesses both of these qualities.ResultsIn this paper, we present the Stochastic Bulirsch-Stoer method, a new numerical method for simulating discrete chemical reaction systems, inspired by its deterministic counterpart. It is able to achieve an excellent efficiency due to the fact that it is based on an approach with high deterministic order, allowing for larger stepsizes and leading to fast simulations. We compare it to the Euler ?-leap, as well as two more recent ?-leap methods, on a number of example problems, and find that as well as being very accurate, our method is the most robust, in terms of efficiency, of all the methods considered in this paper. The problems it is most suited for are those with increased populations that would be too slow to simulate using Gillespie’s stochastic simulation algorithm. For such problems, it is likely to achieve higher weak order in the moments.ConclusionsThe Stochastic Bulirsch-Stoer method is a novel stochastic solver that can be used for fast and accurate simulations. Crucially, compared to other similar methods, it better retains its high accuracy when the timesteps are increased. Thus the Stochastic Bulirsch-Stoer method is both computationally efficient and robust. These are key properties for any stochastic numerical method, as they must typically run many thousands of simulations

Assessment of methods for estimating wild rabbit population abundance in agricultural landscapes

Various methods have been used to estimate rabbit abundance, but comparisons of standard methods are still lacking, and thus, results remain roughly comparable across studies. Ideally, a method should be applicable over a wide range of situations, such as differing abundances or habitat types. Comparisons of methods are required to evaluate the benefits of each of them, and survey methods should be validated for the conditions in which they will be used. In this study, we compare the performance of direct methods (kilometric abundance index and distance sampling) in two seasons and at two times of day (dusk and night) for estimating wild rabbit abundances in agricultural landscapes. Estimates based on direct methods were highly correlated and detected similar seasonal population changes. Night counts provided better estimates than did dusk counts and exhibited more precision. Results are discussed within the context of rabbit behaviour and their implications for rabbit population surveys.Funding was provided by FEDENCA. ICB was supported by a PhD fellowship from the Spanish Ministry of Science and Innovation. PA is currently enjoying a Juan de la Cierva research contract awarded by the Spanish Ministry of Science and Innovation and is supported by the project CGL2006-09567/BOS.Peer Reviewe

Using the Sound Card as a Timer

Experiments in mechanics can often be timed by the sounds they produce. In such cases, digital audio recordings provide a simple way of measuring time intervals with an accuracy comparable to that of photogate timers. We illustrate this with an experiment in the physics of sports: to measure the speed of a hard-kicked soccer ball.Comment: 3 pages, 4 figures, Late

Sucrose homeostasis: Mechanisms and opportunity in crop yield improvement

Sugar homeostasis is a critical feature of biological systems. In humans, raised and dysregulated blood sugar is a serious health issue. In plants, directed changes in sucrose homeostasis and allocation represent opportunities in crop improvement. Plant tissue sucrose varies more than blood glucose and is found at higher concentrations (cytosol and phloem ca. 100 mM v 3.9–6.9 mM for blood glucose). Tissue sucrose varies with developmental stage and environment, but cytosol and phloem exhibit tight sucrose control. Sucrose homeostasis is a consequence of the integration of photosynthesis, synthesis of storage end products such as starch, transport of sucrose to sinks and sink metabolism. Trehalose 6-phosphate (T6P)-SnRK1 and TOR play central, still emerging roles in regulating and coordinating these processes. Overall, tissue sucrose levels are more strongly related to growth than to photosynthesis. As a key sucrose signal,T6P regulates sucrose levels, transport and metabolic pathways to coordinate source and sink at a whole plant level. Emerging evidence shows that T6P interacts with meristems. With careful targeting, T6P manipulation through exploiting natural variation, chemical intervention and genetic modification is delivering benefits for crop yields. Regulation of cereal grain set, filling and retention may be the most strategically important aspect of sucrose allocation and homeostasis for food security

Models for the Assessment of Treatment Improvement: The Ideal and the Feasible

Comparisons of different treatments or production processes are the goals of a significant fraction of applied research. Unsurprisingly, two sample problems play a main role in statistics through natural questions such as. Is the the new treatment significantly better than the old. However, this is only partially answered by some of the usual statistical tools for this task. More importantly, often practitioners are not aware of the real meaning behind these statistical procedures. We analyze these troubles from the point of view of the order between distributions, the stochastic order, showing evidence of the limitations of the usual approaches, paying special attention to the classical comparison of means under the normal model. We discuss the unfeasibility of statistically proving stochastic dominance, but show that it is possible, instead, to gather statistical evidence to conclude that slightly relaxed versions of stochastic dominance hold.Research partially supported by the Spanish Ministerio de Economía y Competitividad y fondos FEDER, grants MTM2014-56235-C2-1-P and MTM2014-56235-C2-2, and by Consejería de Educación de la Junta de Castilla y León, grant VA212U13