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In the regression model with errors in variables, we observe i.i.d. copies of (, ) satisfying = ()+ and =+ involving independent and unobserved random variables , , plus a regression function , known up to a finite dimensional . The common densities of the ’s and of the ’s are unknown, whereas the distribution of is completely known. We aim at estimating the parameter by using the observations ( ...

Consider an autoregressive model with measurement error: we observe = + , where the unobserved is a stationary solution of the autoregressive equation = ( ) + . The regression function is known up to a finite dimensional parameter to be estimated. The distributions of and are unknown and ...

We consider a failure hazard function, conditional on a time-independent covariate , given by ${\eta }_{{\gamma }^0}\left(t\right)f_{{\beta }^0}\left(Z\right)$. The baseline hazard function ${\eta }_{{\gamma }^0}$ and the relative risk $f_{{\beta }^0}$ both belong to parametric families with ${\theta }^0={({\beta }^0,{\gamma }^0)}^{\top }\in {ℝ}^{m+p}$. The covariate has an unknown density and is measured with an error through an additive error model where is a random variable, independent from , with known density $f_{\epsilon }$. We observe a -sample , = 1, ..., , where is the minimum between the failure time and the censoring time, and ...