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Add bootstrap inference options to LDTE/LPTE (partial compliance) - #151
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predict_ldte/predict_lpte (and compute_ldte/compute_lpte) now accept variance_type (moment, multiplier, uniform) and n_bootstrap, matching the inference API of predict_dte/predict_pte. The bootstrap reuses the per-observation influence functions already computed for the analytic variance (multiplier bootstrap), so no models are refit. The default moment path is unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LjhUSYBaHyrPdnQ7z13FKv
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Follow-up to #150 for the optional point in the JOSS review (openjournals/joss-reviews#11047): the partial-compliance methods didn't expose bootstrap inference options, unlike
predict_dte/predict_pte.Changes
predict_ldte/predict_lpteonSimpleLocalDistributionEstimatorandAdjustedLocalDistributionEstimator(and the publiccompute_ldte/compute_lpte) takevariance_typeandn_bootstrap:"moment"(default): the existing analytic pointwise intervals. Results are unchanged."multiplier": pointwise intervals from a multiplier bootstrap."uniform": a band that holds over all locations at once, using a max-t critical value from the multiplier bootstrap. Locations with zero variance (e.g. a CDF at or above the max outcome) are left out of the max.ValueError.mean(influence**2)equals the existing analyticsigmaexactly. That makes"multiplier"a bootstrap of the same estimator as"moment". It uses the same multipliers ascompute_confidence_intervals.variance_typeandn_bootstrapsit beforedisplay_progress, matchingpredict_dte. Calls that passdisplay_progresspositionally would need updating. All in-repo calls use keywords.Testing
"moment""multiplier"half-widths are within 10% of"moment""uniform"band is wider than the pointwise one and contains the estimatevariance_typeraisesruff check .is clean.