TRANSFORMED PARTIAL LEAST SQUARES FOR MULTIVARIATE DATA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The research described herein is motivated by a study of the relationship between agricultural meteorology and three major yields of crops in a province of China. To build a regression model for this data set with multivariate response and high-dimensional covariates, three issues are of particular interest: reducing the dimension of the covariates, avoiding the collinearity between the components of the covariates, and capturing the nonlinearity structure. To deal with these problems, we propose a method of nonparametric response transformation to build a single- index type model, and use partial least squares to reduce the dimension of covariates and to overcome the problem of collinearity. Our method is an alternative approach to sliced inverse regression when the underlying model is single-index type. To select the transformations, a new criterion based on maximizing the covariance matrix is recommended. The selected transformations are estimated by splines; here B- splines are used for general cases and I-splines with a penalty function are suggested when the transformations are monotonic. A modified BIC selection principle is proposed to determine the dimensionality of the space of spline transformations. The consistency of the estimators is proved and easily implemented algorithms are provided. Application to the agricultural data set is carried out.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it