This article is cited in 7 papers

Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients

Yu. Yu. Linke^a, A. I. Sakhanenko<sup>b

a</sup> Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences
^b Ugra State University

Abstract: We consider the problem of estimating the unknown parameter of the one-dimensional analog of the Michaelis–Menten equation when the independent variables are measured with random errors. We study the behavior of the explicit estimates that we have found earlier in the case of known independent variables and establish almost necessary conditions under which the presence of the random errors does not affect the asymptotic normality of these explicit estimates.

Keywords: nonlinear regression, Michaelis–Menten equation, random errors in independent variables, asymptotically normal estimates.

UDC: 519.237.5

Received: 25.04.2003
Revised: 20.11.2007

 English version: Siberian Mathematical Journal, 2008, 49:3, 474–497

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