The ordinary maximum likelihood (ML) approach in classical regression models, fails when the independent variables are subject to error. The most noticeable and well known problem reported in the literature is the inconsistency of the ML estimators [1]. To solve this problem, a number of alternatives were proposed. The measurement error model (MEM) is the most fashionable of them, but it has so...

We consider functional measurement error models where the measurement error distribution is estimated non-parametrically.We derive a locally efficient semiparametric estimator but propose not to implement it owing to its numerical complexity. Instead, a plug-in estimator is proposed, where the measurement error distribution is estimated through non-parametric kernel methods based on multiple me...

Motivated by limitations and capabilities of neutral atom qubits, we examine whether measurement-free error correction can produce practical error thresholds. We show that this can be achieved by extracting redundant syndrome information, giving our procedure extra fault tolerance and eliminating the need for ancilla verification. The procedure is particularly favorable when multiqubit gates ar...