A spline‐based semiparametric sieve likelihood method for over‐dispersed panel count data
Bibliographic record
Abstract
Abstract In this article we study a Gamma‐Frailty inhomogeneous Poisson process model for analysing over‐dispersed panel count data. A cubic B‐spline function is used to approximate the logarithm of the baseline mean function in the semiparametric proportional mean model. The regression parameters and spline coefficients are jointly estimated by maximizing a spline‐based sieve pseudo‐likelihood and by replacing the nuisance over‐dispersion parameter with its moment estimate. The asymptotic properties of the proposed maximum pseudo likelihood estimator, including its consistency, convergence rate and the asymptotic normality of the estimated regression parameters, are thoroughly studied using modern empirical process theory. A spline‐based least‐squares standard error estimator is developed to facilitate robust inference for the regression parameters. Simulation studies are conducted to investigate finite sample performance of the proposed method and robustness of the Gamma‐Frailty inhomogeneous Poisson process model. Finally, for illustration, the method is used to analyse data from an observational study of sexually transmitted infection (STI) in young women. The Canadian Journal of Statistics 42: 217–245; 2014 © 2014 Statistical Society of Canada
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".