Bayesian Model Selection Based on Proper Scoring Rules

Bayesian model selection with improper priors is not well-defined because of the dependence of the marginal likelihood on the arbitrary scaling constants of the within-model prior densities. We show how this problem can be evaded by replacing marginal log-likelihood by a homogeneous proper scoring rule, which is insensitive to the scaling constants. Suitably applied, this will typically enable consistent selection of the true model.Comment: Published at http://dx.doi.org/10.1214/15-BA942 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/

Comparisons of Hyv\"arinen and pairwise estimators in two simple linear time series models

The aim of this paper is to compare numerically the performance of two estimators based on Hyv\"arinen's local homogeneous scoring rule with that of the full and the pairwise maximum likelihood estimators. In particular, two different model settings, for which both full and pairwise maximum likelihood estimators can be obtained, have been considered: the first order autoregressive model (AR(1)) and the moving average model (MA(1)). Simulation studies highlight very different behaviours for the Hyv\"arinen scoring rule estimators relative to the pairwise likelihood estimators in these two settings.Comment: 14 pages, 2 figure

A Note on Bayesian Model Selection for Discrete Data Using Proper Scoring Rules

We consider the problem of choosing between parametric models for a discrete observable, taking a Bayesian approach in which the within-model prior distributions are allowed to be improper. In order to avoid the ambiguity in the marginal likelihood function in such a case, we apply a homogeneous scoring rule. For the particular case of distinguishing between Poisson and Negative Binomial models, we conduct simulations that indicate that, applied prequentially, the method will consistently select the true model.Comment: 8 pages, 2 figure

Theory and Applications of Proper Scoring Rules

We give an overview of some uses of proper scoring rules in statistical inference, including frequentist estimation theory and Bayesian model selection with improper priors.Comment: 13 page

Rejoinder to "Bayesian Model Selection Based on Proper Scoring Rules"

We are deeply appreciative of the initiative of the editor, Marina Vanucci, in commissioning a discussion of our paper, and extremely grateful to all the discussants for their insightful and thought-provoking comments. We respond to the discussions in alphabetical order [arXiv:1409.5291].Comment: Published at http://dx.doi.org/10.1214/15-BA942REJ in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/

Excess of weight: is it a modifiable predictive and prognostic factor in locally advanced rectal cancer?

To evaluate the relationship between body mass index (BMI) and rates of treatment tolerance and clinical outcomes in patients with locally advanced rectal cancer treated with a multimodality approach. PATIENTS AND METHODS: This study was conducted on 56 patients with histologically proven rectal adenocarcinoma, staged T3-4, and/or node-positive tumor, which underwent intensified radiochemotherapy (RT-CHT) treatment before surgery. We calculated adiposity indices and analyzed their influence on treatment tolerance and clinical outcomes. RESULTS: Distribution of the 56 patients according to BMI was BMI < 25 kg/m2 (n = 19; 33.9%), BMI 25-29 kg/m2 (n = 29; 51.8%) and BMI ≥ 30 kg/m2 (n = 8; 14.3%). BMI had no significant influence on neo-adjuvant treatment-related toxicity. With a median follow-up of 23 months (range 11-47), the 2-year survival was 85.7%. We did not observe any significant difference among the three BMI categories for any of the outcomes. CONCLUSIONS: This study suggested no evident links between overweight and survival in patients with locally advanced rectal carcinoma treated with neo-adjuvant RT-CHT. Overweight patients tolerate treatment as normal-weight patients

From statistical evidence to evidence of causality

While statisticians and quantitative social scientists typically study the "effects of causes" (EoC), Lawyers and the Courts are more concerned with understanding the "causes of effects" (CoE). EoC can be addressed using experimental design and statistical analysis, but it is less clear how to incorporate statistical or epidemiological evidence into CoE reasoning, as might be required for a case at Law. Some form of counterfactual reasoning, such as the "potential outcomes" approach championed by Rubin, appears unavoidable, but this typically yields "answers" that are sensitive to arbitrary and untestable assumptions. We must therefore recognise that a CoE question simply might not have a well-determined answer. It is nevertheless possible to use statistical data to set bounds within which any answer must lie. With less than perfect data these bounds will themselves be uncertain, leading to a compounding of different kinds of uncertainty. Still further care is required in the presence of possible confounding factors. In addition, even identifying the relevant "counterfactual contrast" may be a matter of Policy as much as of Science. Defining the question is as non-trivial a task as finding a route towards an answer. This paper develops some technical elaborations of these philosophical points from a personalist Bayesian perspective, and illustrates them with a Bayesian analysis of a case study in child protection

Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer

This study aims to evaluate the feasibility of a magnetic resonance (MR) automatic method for quantitative assessment of the percentage of fibrosis developed within locally advanced rectal cancers (LARC) after neoadjuvant radiochemotherapy (RCT). A total of 65 patients were enrolled in the study and MR studies were performed on 3.0 Tesla scanner; patients were followed-up for 30 months. The percentage of fibrosis was quantified on T2-weighted images, using automatic K-Means clustering algorithm. According to the percentage of fibrosis, an optimal cut-off point for separating patients into favorable and unfavorable pathologic response groups was identified by ROC analysis and tumor regression grade (MR-TRG) classes were determined and compared to histopathologic TRG. An optimal cut-off point of 81% of fibrosis was identified to differentiate between favorable and unfavorable pathologic response groups resulting in a sensitivity of 78.26% and a specificity of 97.62% for the identification of complete responders (CRs). Interobserver agreement was good (0.85). The agreement between P-TRG and MR-TRG was excellent (0.923). Significant differences in terms of overall survival (OS) and disease free survival (DFS) were found between favorable and unfavorable pathologic response groups. The automatic quantification of fibrosis determined by MR is feasible and reproducible

An orthogonal biocatalytic approach for the safe generation and use of HCN in a multistep continuous preparation of chiral O-acetylcyanohydrins

An enantioselective preparation of O-acetylcyanohydrins has been accomplished by a three-step telescoped continuous process. The modular components enabled accurate control of two sequential biotransformations, safe handling of an in situ generated hazardous gas, and in-line stabilization of products. This method proved to be advantageous over the batch protocols in terms of reaction time (40 vs 345 min) and ease of operation, opening up access to reactions which have often been neglected due to safety concerns.We gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG) within the research training group GRK 1166 “Biocatalysis in non-conventional media (BioNoCo)”, and the EPSRC (Award Nos. EP/K009494/1 and EP/K039520/1)This is the final version of the article. It first appeared from Georg Thieme Verlag KG via http://dx.doi.org/10.1055/s-0035-156064