P. Aronow

works on

conditional variance estimation 1 Hölder smoothness 1 minimax rates 1 nonparametric regression 1 random design 1

From the 1 of 4 linked papers with an AI index.

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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

On Rates Attainable under Random Design: A Negative Answer to a Problem of Robins

P. M. Aronow, Patrick Lopatto

The paper proves that in nonparametric regression with random design, the conjectured optimal convergence rate for estimating a constant conditional variance is unattainable, estab…

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…