Detection and Attribution Analysis of Climate Change

Detection and attribution analysis of climate change using errors-in-variables regression. Implements the polynomial shrinkage framework under variance heterogeneity proposed by Li and Li (2026) and the latent factor framework proposed by Li and Li (2026) . Estimates forcing scaling factors and variance inflation coefficients for forced simulations and control runs relative to observations. Selects polynomial covariance weights or bias-corrected spiked precision weights by minimizing estimated total asymptotic scaling factor variance, and provides asymptotic confidence intervals and residual diagnostics. Also retains earlier total least squares and estimating equation procedures (Li et al., 2025, ; Ma et al., 2023, ) for comparison and reproducibility, together with covariance shrinkage estimators.