arXiv subjects

María Dolores Martínez-Miranda

Publications and source records attributed to María Dolores Martínez-Miranda.

3 recordsLinked to original sources

arXiv2025

Low quality exposure and point processes with a view to the first phase of a pandemic

In the early days of a pandemic there is no time for complicated data collection. One needs a simple cross-country benchmark approach based on robust data that is easy to understand and easy to collect. The recent pandemic has shown us what early available pandemic data might look like, because statistical data was published every day in standard news outlets in many countries. This paper provides new methodology for the analysis of data where exposure is only vaguely understood and where the very definition of exposure might change over time. The exposure of poor quality is used to analyse and forecast events. Our example of such exposure is daily infections during a pandemic and the events are number of new infected patients in hospitals every day. Examples are given with French Covid-19 data on hospitalized patients and numbers of infected.

stat.ME↗

arXiv2025

Monitoring a developing pandemic with available data

This paper addresses statistical modelling and forecasting of key indicators describing the severity of a developing pandemic, using routinely reported daily counts of infections, hospitalizations, deaths (both in and out of hospital), and recoveries. These observed counts constitute what we term ``available data''. Because such data are typically incomplete or inconsistently reported, we address several novel missing data challenges arising in this context and propose statistically rigorous solutions that enable inference based solely on the available information. The model is formulated dynamically, explicitly incorporating calendar effects to capture systematic temporal variations in the progression of the pandemic. The proposed framework is illustrated using data from France collected during the COVID-19 pandemic. Our approach also establishes a new benchmark for integrating prior information from domain experts directly into the modelling process, thereby enabling a potential new division of labour between statistical estimation and epidemiological knowledge from external experts.

stat.ME↗

arXiv2016

Bandwidth selection for kernel density estimation with length-biased data

Length-biased data are a particular case of weighted data, which arise in many situations: biomedicine, quality control or epidemiology among others. In this paper we study the theoretical properties of kernel density estimation in the context of length-biased data, proposing two consistent bootstrap methods that we use for bandwidth selection. Apart from the bootstrap bandwidth selectors we suggest a rule-of-thumb. These bandwidth selection proposals are compared with a least-squares cross-validation method. A simulation study is accomplished to understand the behaviour of the procedures in finite samples.

stat.ME↗