Discussion on On the role of data, statistics and decisions in a pandemic

The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of what has been done, what could have been done and what needs to be done. We contribute here with a few comments and some additional issues. We do not discuss all chapters of Jahn et al. (AStA Adv Stat Anal, 2022. 10.1007/s10182-022-00439-7), but focus on those where our personal views and experiences might add some additional aspects

Editorial

Kauermann G, Lang S. Editorial. ASTA-ADVANCES IN STATISTICAL ANALYSIS. 2009;93(3):235-236

Online Monitoring with Local Smoothing Methods and Adaptive Ridging

We consider online monitoring of sequentially arising data as e.g. met in clinical information systems. The general focus thereby is to detect breakpoints, i.e. timepoints where the measurement series suddenly changes the general level. The method suggested is based on local estimation. In particular, local linear smoothing is combined by ridging with local constant smoothing. The procedure is demonstrated by examples and compared with other available online monitoring routines

Flexible Modelling of Duration of Unemployment Using Functional Hazard Models and Penalized Splines: A Case Study Comparing Germany and the UK

The intention of this paper is to demonstrate the flexibility and capacity of penalized spline smoothing as estimation routine for modelling duration time data. We investigate the unemployment behaviour in Germany and the UK between 1995 and 2005 based on data from national panel studies. Functional duration time models are used to investigate the dynamics of covariate effects. The focus of our analysis is on contrasting the two economies. The statistical model being employed is built upon the hazard function, where we allow all covariate effects to vary smoothly with time. As result of the analyses, we demonstrate that the most striking difference between the countries is that elderly unemployed in Germany have decreasing chances for reemployment compared to the UK.

Bicycle Commuting in Melbourne During the 2000s Energy Crisis: A Semiparametric Analysis of Intraday Volumes

Cycling is attracting renewed attention as a mode of transport in western urban environments, yet the determinants of usage are poorly understood. In this paper we investigate some of these using intraday bicycle volumes collected via induction loops located at ten bike paths in the city of Melbourne, Australia, between December 2005 and June 2008. The data are hourly counts at each location, with temporal and spatial disaggregation allowing for the impact of meteorology to be measured accurately for the first time. Moreover, during this period petrol prices varied dramatically and the data also provide a unique opportunity to assess the cross-price elasticity of demand for cycling. Over-dispersed Poisson regression models are used to model volumes at each location and at each hour of the day. Seasonality and the impact of weather conditions are modelled as semiparametric and estimated using recently developed multivariate penalized spline methodology. Unlike previous studies that use aggregate data, the empirical results show a substantial meteorological and seasonal component to usage. They also suggest there was substitution into cycling as a mode of transport in response to increases in petrol prices, particularly during peak commuting periods and by commuters originating in wealthy and inner city neighbourhoods. Last, we extend the approach to a multivariate longitudinal count data model using a Gaussian copula estimated by Bayesian data augmentation. We find first order serial dependence in the hourly volumes and a ‘return trip’ effect in daily bicycle commutes

Filtering Time Series with Penalized Splines

The decomposition and filtering of time series is an important issue in economics and econometrics and related fields. Even though there are numerous competing methods on the market, in applications one often meets one of the few favorites, like the Hodrick-Prescott filter or the bandpass filter.In this paper, we suggest to employ penalized splines fitting for detrending. The approach allows to take correlation of the residuals into account and provides a data driven setting of the smoothing parameter, none of which the classical filters allow. We show the simplicity of the penalized spline filter using the open source software R and demonstrate differences and features with numerous data examples.

Nonparametric small area estimation using penalized spline regression

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