Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Develops and compares consistent estimators for gradual change points in nonparametric regression using a new optimization method targeting the largest minimization point of an objective function, with rates, regression estimation, bootstrap, and two-sample extensions.
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Analysis of gradual changes in nonparametric regression based on a new optimization method in the non-unique case
Develops and compares consistent estimators for gradual change points in nonparametric regression using a new optimization method targeting the largest minimization point of an objective function, with rates, regression estimation, bootstrap, and two-sample extensions.