Continuous-time modeling using Lévy-driven moving averages:Representations, limit theorems and other properties
Bibliographic record
Abstract
progress report as well, Papers D-I are primarily a result of the last two years of my studies.I have contributed comprehensively in both the writing as well as the research phase of Papers A-B and D-H.Together with Victor Rohde I have written Papers C and I, and to these we have contributed equally.The first chapter of the dissertation is an introduction, which motivates the use of Lévy-driven moving averages in the modeling of continuous-time stochastic systems and discusses the importance of obtaining knowledge of their representations, limit theorems and certain other properties.The findings of Papers A-H deliver answers to many of the questions raised in this discussion, and hence the main results of these papers will also be highlighted in this chapter.Paper I, however, is an industrial collaboration with Vestas Wind Systems A/S and concerns estimation of extreme loads on wind turbines using covariates.Since the details are carefully explained in the included paper and the overall aim differs from that of Papers A-H, I have chosen not to address its findings in the introductory chapter.My four years of PhD studies have been both challenging and rewarding, and I owe several people huge thanks for making the journey joyful.First of all, I thank my main supervisor Andreas Basse-O'Connor for giving me the unique opportunity of pursuing a PhD degree in a truly inspiring and intellectually stimulating research environment and for our many fruitful discussions.His support, enthusiasm and high ambitions have definitely pushed my limits as a researcher.A special thanks goes to my co-supervisor Jan Pedersen, with whom I have had uncountably many conversations spanning from technical details in proofs and general probabilistic and statistical considerations to an analysis of the outcome of yesterday's hockey match.Due to his extraordinary guidance, his trust in my abilities and his positive mindset, Jan has had a significant impact on my development and well-being during my studies.I feel honored that Andreas and Jan have invested this much time and effort in me-it exceeds by far what could be expected of a supervisor, and for this I am deeply grateful.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".