Disputation: Emil Aas Stoltenberg
Doctoral candidate Emil Aas Stoltenberg at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, is defending the thesis Epidemiological, Econometric, and Decision Theoretic Applications of Statistical Inference for the degree of Philosophiae Doctor.
Doctoral candidate Emil Aas Stoltenberg
The University of Oslo is closed. The PhD defence and trial lecture will therefore be digital and streamed directly using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.
Ex auditorio questions: the chair of the defence will invite the audience to ask ex auditorio questions either written or oral. This can be requested by clicking 'Participants -> Raise hand'.
The webinar opens for participation just before the disputation starts, participants who join early will be put in a waiting room.
Submit the request to get access to the thesis
30th October, 10:15, Zoom
"Stable convergence with applications"
Join the trial lecture
The webinar opens for participation just before the trial lecture starts, participants who join early will be put in a waiting room.
Main research findings
This thesis presents new statistical methodology, models, and estimators that are geared towards answering scientific questions in epidemiology and medicine, and in economics and finance. The questions are: (i) What is the effect of gestational exposure to paracetamol on the neurodevelopment of the offspring? (ii) What is the effect of improving sanitary systems on the health of small children? (iii) How does the trading intensity of a stock correlate with the volatility of this stock? On one level, these questions may appear to have little in common. On a statistical methodology level, however, they are related: They all involve stochastic processes evolving in time, and these processes are only partially observed at potentially random points in time. This means that when trying to answer the questions above, one is faced with similar challenges when going from the data to claims about the unknown parameters determining the behaviour of the stochastic processes, as well as when one is to attach uncertainty to these claims. The kinship between the statistical models also makes it possible to transfer methods developed for one type of models, to others. One of the articles in the thesis is a case in point, in that it takes a method originally developed for high-frequency financial data, and applies it to solve a problem for a class of statistical models often used in epidemiology.