Bibliographic record
Abstract
Chapter 2, co-authored with Vadim Marmer and Kyungchul Song, considers a general form of network dependence, where dependence between two sets of random variables becomes weaker as their network distance increases. We show that such network dependence cannot be viewed as a random field on a lattice in a Euclidean space with a fixed dimension when the maximum clique increases in size as the network grows. This work applies Doukhan and Louhichi (1999)’s notion of weak dependence to networks by measuring the strength of dependence using the covariance between nonlinearly transformed random variables. While this approach covers examples such as strong mixing random fields on graphs and conditional dependency graphs, it is most useful when dependence arises through a functional-causal system of equations. The main results of this chapter include a law of large numbers and a central limit theorem for network dependent processes. Chapter 3 focuses on the bootstrap for network dependent processes studied in Chapter 2. Such processes are distinct from other forms of random fields that are commonly used in the statistics and econometrics literature so that the existing bootstrap methods cannot be applied directly. I propose a block-based method and a modification of the dependent wild bootstrap for constructing confidence sets for the mean of a network dependent process. In addition, I establish the consistency of these methods for the smooth function model and provide the bootstrap alternatives to the network heteroskedasticity-autocorrelation consistent variance estimator obtained in Chapter 2. Finally, Chapter 4, co-authored with Kyungchul Song, presents a large Bayesian game with multiple information groups and develops a bootstrap inference method that does not require a common prior assumption and allows each player to form beliefs differently from other players. By drawing on the intuition of Kalai (2004), this work introduces the notion of a hindsight regret, which measures a player’s ex post value of other players’ type information, and obtains its belief-free bound. Using this bound, we derive testable implications and propose a bootstrap inference procedure for the structural parameters of the game.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".