1,721,238 research outputs found
The Contribution of Young Researchers to Bayesian Statistics
No full textPlenary lecturers and senior discussants of BAYSM 2013 provide a discussion of recent developments in Bayesian statistics
Highlights contributions of young researchers to BAYSM 2013 through comprehensive research papers
Includes suggested research topics and predictions for the future of Bayesian statistics
The first Bayesian Young Statisticians Meeting, BAYSM 2013, has provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and post-docs dealing with Bayesian statistics to connect with the Bayesian community at large, exchange ideas, and network with scholars working in their field. The Workshop, which took place June 5th and 6th 2013 at CNR-IMATI, Milan, has promoted further research in all the fields where Bayesian statistics may be employed under the guidance of renowned plenary lecturers and senior discussants. A selection of the contributions to the meeting and the summary of one of the plenary lectures compose this volume
Outlier detection for training sets in an unsupervised functional classification framework: an application to ECG signals
No full textProceedings of the European Young Statisticians Meetin
Component-wise outlier detection methods for robustifying multivariate functional samples
No full textWe propose a new method for detecting outliers in multivariate functional data. We exploit the joint use of two different depth measures, and generalize the outliergram to the multivariate functional framework, aiming at detecting and discarding both shape and magnitude outliers. The main application consists in robustifying the reference samples of data, composed by G different known groups to be used, for example, in classification procedures in order to make them more robust. We asses by means of a simulation study the method’s performance in comparison with different outlier detection methods. Finally we consider a real dataset: we classify data minimizing a suitable distance from the center of reference groups. We compare performance of supervised classification on test sets training the algorithm on original dataset and on the robustified one, respectively
Statistical Tools for Detecting and Visualizing Outliers in Provider Profiling: an effective decisional support to healthcare regulation
No full textWe propose the use of a graphical diagnostic tool (the funnel plot) to detect outliers among hospitals that treat patients affected by Acute Myocardial Infarction (AMI). We consider an application to data on AMI hospitalizations arising from administrative databases. The outcome of interest is the
in-hospital mortality, a variable indicating if the patient has been discharged dead or alive. We then compare the results obtained by graphical diagnostic tools with those arising from fitting parametric mixed effects models to the same data
Risk Prediction for Myocardial Infarction via Generalized Functional Regression Models.
No full textIn this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of Electrocardiographic (ECG) traces of patients whose pre-hospital ECG has been sent to 118 Dispatch Center ofMilan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a Multivariate Functional Principal Component Analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally the robustness of the procedure is checked via leave-j-out techniques
Statistical Analysis of an integrated Database concerning patients with Acute Coronary Syndromes
No full textMulti-state models for the joint prediction of time to hospitalizations and death in heart failure patients
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