Bayesian inference on group differences in multivariate categorical data

Multivariate categorical data are common in many fields. We are motivated by election polls studies assessing evidence of changes in voters opinions with their candidates preferences in the 2016 United States Presidential primaries or caucuses. Similar goals arise routinely in several applications, but current literature lacks a general methodology which combines flexibility, efficiency, and tractability in testing for group differences in multivariate categorical data at different---potentially complex---scales. We address this goal by leveraging a Bayesian representation which factorizes the joint probability mass function for the group variable and the multivariate categorical data as the product of the marginal probabilities for the groups, and the conditional probability mass function of the multivariate categorical data, given the group membership. To enhance flexibility, we define the conditional probability mass function of the multivariate categorical data via a group-dependent mixture of tensor factorizations, thus facilitating dimensionality reduction and borrowing of information, while providing tractable procedures for computation, and accurate tests assessing global and local group differences. We compare our methods with popular competitors, and discuss improved performance in simulations and in American election polls studies

Characterized subgroups of topological abelian groups

A subgroup $H$ of a topological abelian group $X$ is said to be characterized by a sequence $\mathbf v =(v_n)$ of characters of $X$ if $H=\{x\in X:v_n(x)\to 0\ \text{in}\ \mathbb T\}$. We study the basic properties of characterized subgroups in the general setting, extending results known in the compact case. For a better description, we isolate various types of characterized subgroups. Moreover, we introduce the relevant class of autochacaracterized groups (namely, the groups that are characterized subgroups of themselves by means of a sequence of non-null characters); in the case of locally compact abelian groups, these are proved to be exactly the non-compact ones. As a by-product of our results, we find a complete description of the characterized subgroups of discrete abelian groups.Comment: 22 page

Locally adaptive factor processes for multivariate time series

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inferences and predictions, with over-smoothing across erratic time intervals and under-smoothing across times exhibiting slow variation. This can lead to mis-calibration of predictive intervals, which can be substantially too narrow or wide depending on the time. We propose a locally adaptive factor process for characterizing multivariate mean-covariance changes in continuous time, allowing locally varying smoothness in both the mean and covariance matrix. This process is constructed utilizing latent dictionary functions evolving in time through nested Gaussian processes and linearly related to the observed data with a sparse mapping. Using a differential equation representation, we bypass usual computational bottlenecks in obtaining MCMC and online algorithms for approximate Bayesian inference. The performance is assessed in simulations and illustrated in a financial application

A LabVIEW environment to compensate temperature-driven fluctuations in the signal from continuously running spring gravimeters

Environmental parameters can seriously affect the performances of continuously running spring gravimeters. Temperature is a primary interfering quantity and its effect must be reduced through algorithms implementing a suitable compensation scheme. Algorithms to reduce the signals coming from continuously running gravimeters for the effect of meteorological perturbations have been developed and implemented in tools running in offline-mode. Anyway, the need for ‘‘on–the-fly’’ processing emerges when the recorded signals are used for volcano monitoring purposes, since any information on the volcanic phenomena under development must be assessed immediately. In this paper the implementation, in a dedicated LabVIEW application, of an algorithm performing temperature reduction on gravity signals is discussed and features of the software’s user interface are presented

Acer-Fraxinus dominated woods of the Italian Peninsula: a floristic and phytogeographical analysis.

Forest communities dominated by noble broad-leaved trees (maple, lime and ash) in Europe are of elevated scientific and con­servation interest for the European Union. In this paper, we first present a synthesis of the maple and ash forests in peninsular Italy. By classifying these forests, we distinguish seven main groups for the territory, which only broadly match the syntaxa proposed in the literature. The variability of the Apennine data is then analysed floristically and phytogeographically (using chorological components) in a central-southern European context, using numerical classification, INSPAN, and direct ordination of several synoptic tables. These analyses allow us to identify six different groups of European Acer-Fraxinus communities. Canonical Vari­ates Analysis (CVA) of the geographical components confirms the existence of distinct phytogeographical groups. In particular, we highlight the clear distinction between central European (including the Alps) and southern European coenoses. Among the latter there was a clear floristic and chorological distinction between Balkan and Apennine groups. These results reflect the biogeographical subdivisions of Europe, but do not support the syntaxonomical schemes proposed by other authors, which are based only on floristic-ecological information or (recently) use a smaller data set of Italian relevés. This study also shows that syntaxonomical schemes above the association level should pay more attention to phytogeographical aspects rather than focus on floristic-ecological information alone, in order to propose models that are of value on a geographical scale

Taxanes in adjuvant chemotherapy for early breast cancer.

Adjuvant polychemotherapy improves diseasefree survival and overall survival in women with early breast cancer. A meta-analysis by the Early Breast Cancer Trialists' Collaborative Group (EBCTCG) reported that over 15 years there had been a reduction in recurrence and death in women younger than 50 years who had received adjuvant polychemotherapy [1]. A smaller but still highly significant reduction in the risk of recurrence and death was observed for women aged 50–69 years who received the same treatment. The effect of adjuvant chemotherapy on recurrence was noted mainly during the first 5 years after randomization. The magnitude of effect within this 5-year period was 2.5-times greater for women aged under 50 years compared with women aged 50–59 years. The EBCTCG meta-analysis also compared regimens that contain anthracyclines with no chemotherapy or with the oral combination of cyclophosphamide, methotrexate and 5-fluorouracil (CMF) [1]. The most widely investigated regimens that contain anthracyclines were a combination of cyclophosphamide and 5-fluorouracil with either doxorubicin or epirubicin. The EBCTCG study found that allocation to approximately 6 months of anthracycline-based polychemotherapy reduced the yearly death rate from breast cancer by approximately 38% for women younger than 50 years of age at diagnosis and by approximately 20% for women aged 50–69 years at diagnosis