P. M. Aronow

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math.ST2026

Adaptive Confidence Sets for Binary Regression without Design Smoothness

P. M. Aronow, Patrick Lopatto

We study honest adaptive confidence sets for the regression function in random-design binary regression under $L^{2} (d x)$ loss. Assuming only known bounds $0 < c \leq g \leq C < \infty$ on…

math.ST2026

Nearly-minimax variance estimation under rough random design

P. M. Aronow, Patrick Lopatto

We identify the minimax exponent for constant conditional variance estimation under rough random design. The unknown design density is bounded above and away from zero, with no smo…

math.ST2026

Minimax unbiased estimation for finite populations with bounded outcomes

P. M. Aronow, Patrick Lopatto

We study design-unbiased estimation of the finite-population total $\sum_{i = 1}^{N} y_{i}$ when each outcome satisfies known bounds $y_{i} \in [a_{i}, b_{i}]$ . For any sampling design with inclu…