Competing with stationary prediction strategies

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.Comment: 20 page

Leading strategies in competitive on-line prediction

We start from a simple asymptotic result for the problem of on-line regression with the quadratic loss function: the class of continuous limited-memory prediction strategies admits a "leading prediction strategy", which not only asymptotically performs at least as well as any continuous limited-memory strategy but also satisfies the property that the excess loss of any continuous limited-memory strategy is determined by how closely it imitates the leading strategy. More specifically, for any class of prediction strategies constituting a reproducing kernel Hilbert space we construct a leading strategy, in the sense that the loss of any prediction strategy whose norm is not too large is determined by how closely it imitates the leading strategy. This result is extended to the loss functions given by Bregman divergences and by strictly proper scoring rules.Comment: 20 pages; a conference version is to appear in the ALT'2006 proceeding

Somatostatin receptor 2A in gliomas: Association with oligodendrogliomas and favourable outcome

Somatostatin receptor subtype 2A (SSTR2A) is a potential therapeutic target in gliomas. Data on SSTR2A expression in different glioma entities, however, is particularly conflicting. Our objective was to characterize SSTR2A status and explore its impact on survival in gliomas classified according to the specific molecular signatures of the updated WHO classification. In total, 184 glioma samples were retrospectively analyzed for SSTR2A expression using immunohistochemistry with monoclonal antibody UMB-1. Double staining with CD68 was used to exclude microglia and macrophages from analyses. SSTR2A staining intensity and its localization in tumor cells was evaluated and correlated with glioma entities and survival. Diagnoses included 101 glioblastomas (93 isocitrate dehydrogenase (IDH) -wildtype, 3 IDH-mutant, 5 not otherwise specified (NOS)), 60 astrocytomas (22 IDH-wildtype, 37 IDH-mutant, 1 NOS), and 23 oligodendrogliomas (19 IDH-mutant and 1p/19q-codeleted, 4 NOS). SSTR2A expression significantly associated with oligodendrogliomas (79% SSTR2A positive) compared to IDH-mutant or IDH-wildtype astrocytomas (27% and 23% SSTR2A positive, respectively), and especially glioblastomas of which only 13% were SSTR2A positive (p < 0.001, Fisher's exact test). The staining pattern in glioblastomas was patchy whereas more homogeneous membranous and cytoplasmic staining was detected in oligodendrogliomas. Positive SSTR2A was related to longer overall survival in grade II and III gliomas (HR 2.7, CI 1.2-5.8, p = 0.013). In conclusion, SSTR2A expression is infrequent in astrocytomas and negative in the majority of glioblastomas where it is of no prognostic significance. In contrast, oligodendrogliomas show intense membranous and cytoplasmic SSTR2A expression, which carries potential diagnostic, prognostic, and therapeutic value

Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity

We study the problem of aggregation under the squared loss in the model of regression with deterministic design. We obtain sharp PAC-Bayesian risk bounds for aggregates defined via exponential weights, under general assumptions on the distribution of errors and on the functions to aggregate. We then apply these results to derive sparsity oracle inequalities

Beta diversity patterns reveal positive effects of farmland abandonment on moth communities

Farmland abandonment and the accompanying natural succession are largely perceived as unwanted amongst many European conservationists due to alleged negative effects on biodiversity levels. Here, we test this assumption by analysing alpha, beta and gamma diversity patterns of macro-moth communities in habitats on an ecological succession gradient, from extensively managed meadows to scrub-encroached and wooded sites. Macro-moths were light-trapped at 84 fixed circular sampling sites arranged in a semi-nested design within the National Park of Peneda-Gerês, NW-Portugal. In total, we sampled 22825 individuals belonging to 378 species. Alpha, beta and gamma diversity patterns suggest that farmland abandonment is likely to positively affect both overall macro-moth diversity and forest macro-moth diversity, and to negatively affect species diversity of non-forest macro-moth species. Our results also show that spatial habitat heterogeneity is important to maintain gamma diversity of macromoths, especially for rare non-forest species and habitat specialistsinfo:eu-repo/semantics/publishedVersio