Selected work

Representative Papers

A Comparison for Non-Specialists of Workflow Steps and Similarity of Factor Rankings for Several Global Sensitivity Analysis Methods

Oct 24, 2025

This study addresses non-expert users by systematically evaluating the workflow feasibility and factor ranking consistency of multiple global sensitivity analysis (GSA) methods across simulation models of varying complexity. Method: It integrates Sobol’ first-order and total-effect indices with regression tree analysis, and—novelty—employs Kendall’s W to quantify inter-method ranking similarity; special attention is given to how parameter range specification affects result robustness. Contribution/Results: (1) Major GSA methods exhibit high consistency in factor importance ranking; (2) Sobol’ indices offer both interpretability and information richness, while regression trees effectively detect interaction effects; (3) Parameter range specification is identified as a critical practical determinant of GSA reliability. Collectively, these findings significantly enhance operationality and methodological rationality for non-experts in tasks such as factor screening, freezing, and prioritization.

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Modelling phenology using ordered categorical generalized additive models

Aug 11, 2025

Phenological data inherently possess an ordinal categorical structure, yet conventional continuous-distribution or binary models often neglect this property, leading to biased ecological inference. To address this, we propose an Ordinal Generalized Additive Model (O-GAM) implemented in R using the *mgcv* package— the first to integrate an ordinal logistic link function with smooth terms, thereby explicitly preserving the ordered nature of phenological stages while flexibly modeling nonlinear effects of environmental covariates. The method supports Bayesian confidence interval estimation and systematic residual diagnostics, enhancing interpretability and robustness. Applied to Greenland saxifrage phenology data, O-GAM accurately detects phenological trends, quantifies environmental drivers, and generates ecologically meaningful derived metrics. This approach establishes a new paradigm for phenological modeling that balances statistical rigor with ecological plausibility.

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Recent publications

Latest Papers

A Comparison for Non-Specialists of Workflow Steps and Similarity of Factor Rankings for Several Global Sensitivity Analysis Methods

Oct 24, 2025

This study addresses non-expert users by systematically evaluating the workflow feasibility and factor ranking consistency of multiple global sensitivity analysis (GSA) methods across simulation models of varying complexity. Method: It integrates Sobol’ first-order and total-effect indices with regression tree analysis, and—novelty—employs Kendall’s W to quantify inter-method ranking similarity; special attention is given to how parameter range specification affects result robustness. Contribution/Results: (1) Major GSA methods exhibit high consistency in factor importance ranking; (2) Sobol’ indices offer both interpretability and information richness, while regression trees effectively detect interaction effects; (3) Parameter range specification is identified as a critical practical determinant of GSA reliability. Collectively, these findings significantly enhance operationality and methodological rationality for non-experts in tasks such as factor screening, freezing, and prioritization.

0 citationsRead paper

Modelling phenology using ordered categorical generalized additive models

Aug 11, 2025

Phenological data inherently possess an ordinal categorical structure, yet conventional continuous-distribution or binary models often neglect this property, leading to biased ecological inference. To address this, we propose an Ordinal Generalized Additive Model (O-GAM) implemented in R using the *mgcv* package— the first to integrate an ordinal logistic link function with smooth terms, thereby explicitly preserving the ordered nature of phenological stages while flexibly modeling nonlinear effects of environmental covariates. The method supports Bayesian confidence interval estimation and systematic residual diagnostics, enhancing interpretability and robustness. Applied to Greenland saxifrage phenology data, O-GAM accurately detects phenological trends, quantifies environmental drivers, and generates ecologically meaningful derived metrics. This approach establishes a new paradigm for phenological modeling that balances statistical rigor with ecological plausibility.

0 citationsRead paper