Seminars

Bayesian nonparametric spectral analysis for multivariate time series has received much attention in the past few decades. However, most of studies on approximating the posterior for spectral density are based on Markov chain Monte Carlo (MCMC) and its derived variants. This is an accurate approach, but when the data size is slightly larger or the model is very complex, the computational cost increases significantly. This study analyses a novel efficient method which combines the stochastic optimization and variational inference as simulation process for spectral density. The effect of the learning rate of stochastic optimization on the spectral density is started to be explored. Through the modification and study of its hyperparameters, it has the possibility to simulate the spectral density for large size of multivariate time series.

Jianan Liu is a PhD student and this is his PYR seminar.

https://profiles.auckland.ac.nz/jliu812

At the Federal University of Rio Grande (FURG), in the extreme south of Brazil, at the Centre for Innovation in Statistical Education (ICE), research in statistical education is developed with the aim of qualifying teaching and learning in this area of knowledge. In order to contribute to the advancement of scientific knowledge, projects of technological development, applied research, teaching and university extension have been initiated. The results of these research projects indicate pathways for the qualification of teaching at universities and schools. The scientific findings synthesize knowledge across the scopes of Teaching Knowledge, Playfulness, Student Protagonism, Social Justice, Interdisciplinarity, among others. In this seminar, results from these Brazilian statistics education research projects will be presented. The talk will contemplate the context, the theoretical background, the design methodology, and highlight the scientific findings themselves. There will also be opportunity for dialogue.

Dr Porciuncula is an Associate Professor at the Federal University of Rio Grande - FURG. Her research interest focuses on statistical education. https://ppgec.furg.br/index.php/corpo-docente/docentes-permanentes/301-mauren-porciuncula.html

We explore the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We present a novel two-way functional median polish decomposition, which is robust against outliers, to decompose HDFTS into deterministic and time-varying components. A functional time series forecasting method, based on dynamic functional principal component analysis, is implemented to produce forecasts for the time-varying components. By combining the forecasts of the time-varying components with the deterministic components, we obtain forecast curves for multiple populations. We apply the model to age- and sex-specific mortality rates in the US, France, and Japan, in which there are 51 states, 95 departments, and 47 prefectures, respectively, to illustrate that the proposed model delivers more accurate point and interval forecasts in forecasting multi-population mortality than several benchmark methods.

Health information techniques, including electronic health records (EHRs), have been widely adopted by medical systems across the globe, offering patients more effective, safer, and enhanced-quality care. However, the presence of measurement errors in EHRs not only introduces individual information biases but also has the potential to result invalidate statistical inferences in medical studies, thereby posing a significant challenge to data analysis. Human validation of electronic health record data can improve the quality but is impractical at large scale, so validation of subsamples is an active research area.

I will investigate multiple imputation as an approach to inference with validation subsamples. Parametric or semi-parametric imputation approaches require the user to manually select the ideal statistical model for imputation, which limits the user must have a strong background in statistics and data analysis. The goal is to address this restriction by incorporating semi-automated imputation techniques based on machine learning to measurement-error problems with a validated sub-sample.

In this talk, I will describe my two most recent statistical problems and solutions related to the National Basketball Association (NBA). In particular, I will discuss (1) the usefulness of a coach calling a timeout to thwart an opposition’s momentum and (2) a novel metric for rating the overall shooting effectiveness of players in the NBA. I will describe the motivation for each problem, how to find data for NBA analyses, modeling considerations, and our results. Lastly, I will describe why I think the analysis of sport, in general, provides an ideal venue for teaching/learning statistical or analytical concepts and techniques.

Humans learn about the world around them by interacting with it. The same applies to data. If we want to learn from our data effectively, we need practical tools for creating and manipulating data visualizations. Currently, there are many options for interactive data visualization within the statistical programming ecosystem, in languages such as R or Python. However, all of the currently available software packages tend to suffer from a common set of drawbacks. Specifically, these packages tend to be either very high-level, such that the users are limited to picking from a small set of ready-made interactive plots, or very low-level, such that a lot of time and effort is required to create even moderately complex interactive figures, and there are no guarantees that the interaction will be predictable or composable. The goal of the presented research is to develop a mid-level framework that would allow the users to create entirely new types of interactive plots, which would nevertheless be guaranteed to behave in consistent ways when combined. To this end, the project incorporates concepts from category theory, an area of mathematics concerned with structure and composition. The findings so far show that, by requiring that the statistical summaries we draw conform to a small set of desirable properties, we can guarantee consistent two-way interaction. The project also seeks to implement the system, and live demostration of a prototype will take place as part of the talk.

The research is motivated by the increased interest in detecting possible

frauds in healthcare systems. We propose some unsupervised statistical

tools (Lorenz curve, concentration function, sum of ranks, Gini and Pietra

indices) to provide efficient and easy-to-use methods aimed to signal

possible anomalous behaviours. A more sophisticated method, based on

Bayesian co-clustering, is presented as well.

In observational, non-randomized studies, groups usually differ in some baseline covariates. Propensity scores are increasingly being used in the statistical analysis to adjust for those between-group variations. There is great flexibility in how the propensity score can be appropriately used. One possible strategy is stratification, also called subclassification. We present examples and discuss the question how many strata are useful.

Moreover, the flexibility might encourage p-value hacking – where several alternative uses of propensity scores are explored and the one yielding the lowest p-value is selectively reported. Although such an approach is scientifically not acceptable, it might occur and therefore we simulate the extent of type I error inflation.

For decades now, statistics educators have worked to achieve wide-spread statistical literacy. And now, well before the task is accomplished, along comes Data Science Education. I’ll explain why, from my perspective, this term is more than just a new label for an old thing, describe updates to the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE) Pre-K-12 report, and give a brief overview of a high school data science course that I helped design and propagate. I’ll also discuss currents in the US pushing back against data science (and statistics) education.

Authors: Jorge González-Ortega, David Ríos Insua, Javier Cano

Abstract: We describe how to support a decision maker who faces an adversary. To that end, we consider general interactions entailing sequences of both agents’ decisions, some of them possibly being simultaneous or repeated across time. We model their joint problem as a bi-agent influence diagram. Unlike previous solutions framed under a standard game-theoretic perspective, we provide a decision-analytic methodology to support the decision maker based on an adversarial risk analysis paradigm. This allows the avoidance of non-realistic strong common knowledge assumptions typical of non-cooperative game theory as well as a better apportion of uncertainty sources. We illustrate the methodology with a schematic critical infrastructure protection problem.

DOI: https://doi.org/10.1016/j.ejor.2018.09.015