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develop a deconvolution estimator and show that it is minimax optimal and adaptive in the case of supersmooth error …
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expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution …
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In this paper we develop a simple maximum likelihood estimator for probit models where the regressors have measurement error. We first assume precise information about the reliability ratios (or, equivalently, the proxy correlations) of the regressors. We then show how reasonable bounds for the...
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Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g., validation data, known error distributions, repeated measurements or instruments). In contrast, we establish that the fully nonparametric classical...
Persistent link: https://www.econbiz.de/10010318690
Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g., validation data, known error distributions, repeated measurements or instruments). In contrast, we establish that the fully nonparametric classical...
Persistent link: https://www.econbiz.de/10009669584