We develop the first nonparametric significance test for regression models with classical measurement error in regressors. In particular, a Cramér-von Mises and Kolmogorov–Smirnov null hypothesis $E\left [Y|X^{*},Z^{*}\right ]=E\left [Y|X^{*}\right ]$ are proposed when only noisy measurements of $X^{*}$ $Z^{*}$ available. The asymptotic distributions statistics derived, bootstrap method is impl...

The presence of measurement error is a widespread issue, which, when ignored, can render the results an analysis unreliable. Numerous corrections for effects have been proposed and studied, often under assumption normally distributed, additive measurement-error model. In many situations, observed data are nonsymmetric, heavy-tailed, or otherwise highly non-normal. these settings, correction tec...

Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative ...

Algorithms for causal discovery emerged in the early 1990s and have since proliferated [4, 10]. After directed acyclic graphical representations of causal structures (causal graphs) were connected to conditional independence relations (the Causal Markov Condition1 and dseparation2), graphical characterizations of Markov equivalence classes of causal graphs (patterns) soon followed, along with p...

Measurement-based (or one-way) quantum error correction (MBQEC) is a method with the capability to detect and correct any errors present in a measurement-based quantum computation (MBQC) setup [1]. There are a variety of methods and protocols we can use to perform QEC, although few that have been successfully implemented experimentally [2, 3]. An MBQC protocol requires a resource state and will...

Abstract Positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and keeping alveoli open. However, there is no standard means to find an optimal patient-specific PEEP, creating variability care outcomes. There thus a need for personalized approaches the best PEEP optimise care. This research extends well-validated virtual patient model with newly proposed functi...