Bibliographic record
Abstract
It is now the modern trend and reality in various fields of life and science that the data sets to be analyzed are high dimensional, and the number of observations is much smaller than their dimension.As the classical statistical methods are not designed to deal with big or high-dimensional data, the problem of developing new methods of high-dimensional statistical analysis is very important.In many statistical applications such as analysis of microarray data, signal recovery, and functional magnetic resonance imaging, the focus is often on identifying and estimating a relatively few significant components from a high-dimensional vector.In this thesis, we study the problem of component or variable selection in a normal mixture model based on a single high-dimensional observation.The goal is to examine the possibilities and limitations of optimal component selection in a two-point normal mixture model and, whenever possible, to construct optimal (nonimprovable) selection procedures.In addition to that, the problem of estimating an unknown parameter that determines the sparsity pattern of the data is addressed.The main theoretical findings of the thesis obtained in Chapter 1, and partly in Chapter 2, are supported by the simulation study; the results of the simulation study are presented in Chapter 3. The main results of the thesis, Theorems 2-5, are new.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".