Publications (13)4.02 Total impact
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ABSTRACT: We study asymptotic behavior of onestep weighted $M$estimators based on samples from arrays of not necessarily identically distributed random variables and representing explicit approximations to the corresponding consistent weighted $M$estimators. Sufficient conditions are presented for asymptotic normality of the onestep weighted $M$estimators under consideration. As a consequence, we consider some wellknown nonlinear regression models where the procedure mentioned allow us to construct explicit asymptotically optimal estimators. 
Article: Asymptotic properties of onestep $M$estimators based on nonidentically distributed observations
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ABSTRACT: We study asymptotic behavior of onestep $M$estimators based on samples from arrays of not necessarily identically distributed random variables and representing explicit approximations to the corresponding consistent $M$estimators. These estimators generalize Fisher's onestep approximations to consistent maximum likelihood estimators. Sufficient conditions are presented for asymptotic normality of the onestep $M$estimators under consideration. As a consequence, we consider some wellknown nonlinear regression models where the procedure mentioned allow us to construct explicit asymptotically optimal estimators.  [Show abstract] [Hide abstract]
ABSTRACT: We study the accuracy of estimation of unknown parameters in the case of twostep statistical estimates admitting special representations. An approach to the study of such problems previously proposed by the authors is extended to the case of the estimation of a multidimensional parameter. As a result, we obtain necessary and sufficient conditions for the weak convergence of the normalized estimation error to a multidimensional normal distribution.  [Show abstract] [Hide abstract]
ABSTRACT: In this article, we consider the problem of finding a solution to a functional equation which in a special way depends on the distribution of a random variable. Such equations naturally arise in construction of consistent estimates in regression problems in the case when the variances of the main observations depend on underlying unknown parameter and the regression coefficients are determined with random errors. A simple example of a regression problem is demonstrated when the equation under consideration occurs.  [Show abstract] [Hide abstract]
ABSTRACT: We consider the linear regression model in the case when the independent variables are measured with errors, while the variances of the main observations depend on an unknown parameter. In the case of normally distributed replicated regressors we propose and study new classes of twostep estimates for the main unknown parameter. We find consistency and asymptotic normality conditions for firststep estimates and an asymptotic normality condition for secondstep estimates. We discuss conditions under which these estimates have the minimal asymptotic variance. Keywordslinear regression–errors in independent variables–replicated regressors–dependence of variances on a parameter–twostep estimates–consistent estimate–asymptotically normal estimate  [Show abstract] [Hide abstract]
ABSTRACT: We study the twostep statistical estimates that admit certain expressions of a sufficiently general form. These constructions arise in various statistical models, for instance in regression problems. Under rather weak restrictions we find necessary and sufficient conditions for the normalized difference of a twostep estimate and the unknown parameter to converge weakly to an arbitrary distribution. 
Article: Improvement of estimators in a linear regression problem with random errors in coefficients
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ABSTRACT: Under consideration is the problem of estimating the linear regression parameter in the case when the variances of observations depend on the unknown parameter of the model, while the coefficients (independent variables) are measured with random errors. We propose a new twostep procedure for constructing estimators which guarantees their consistency, find general necessary and sufficient conditions for the asymptotic normality of these estimators, and discuss the case in which these estimators have the minimal asymptotic variance. Keywordslinear regression–errors in the independent variables–dependence of variance on a parameter–twostep estimation–asymptotically normal estimator  [Show abstract] [Hide abstract]
ABSTRACT: We consider the problem of estimating the unknown parameters of linear regression in the case when the variances of observations depend on the unknown parameters of the model. A twostep method is suggested for constructing asymptotically linear estimators. Some general sufficient conditions for the asymptotic normality of the estimators are found, and an explicit form is established of the best asymptotically linear estimators. The behavior of the estimators is studied in detail in the case when the parameter of the regression model is onedimensional.  [Show abstract] [Hide abstract]
ABSTRACT: We consider the problem of estimating the unknown parameter of the onedimensional analog of the MichaelisMenten 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. 
Article: Asymptotically optimal estimation in a linear regression problem with random errors in coefficients
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ABSTRACT: We consider the problem of estimating an unknown onedimensional parameter in the linear regression problem in the case when the independent variables (called coefficients in this article) are measured with errors, and the variances of the principal observations can depend on the main parameter. We study the behavior of twostep estimators, previously introduced by the authors in ibid. 50, No. 2, 302–315 (2009), which are asymptotically optimal in the case when the independent variables are measured without errors. Under sufficiently general assumptions we find necessary and sufficient conditions for the asymptotic normality and asymptotic optimality of these estimators in the new setup.  [Show abstract] [Hide abstract]
ABSTRACT: Under consideration is the problem of estimating unknown parameters in the Michaelis–Menten equation which is frequent in natural sciences. The authors suggest and study asymptotically normal explicit estimates of unknown parameters which often have a minimal covariance matrix. 
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ABSTRACT: Suppose that in some experiment we observe a sequence of independent random variables X1, X2,..., XN such that the following representation is valid for every i:
Publication Stats
44  Citations  
4.02  Total Impact Points  
Top Journals
Institutions

20012012

Sobolev Institute of Geology and Mineralogy
NovoNikolaevsk, Novosibirsk, Russia


2011

Novosibirsk State University
NovoNikolaevsk, Novosibirsk, Russia
