TY - JOUR

T1 - Constructing explicit estimators in nonlinear regression problems

AU - Linke, Yu Yu

AU - Borisov, I. S.

N1 - Funding Information: ∗Received by the editors February 24, 2016. This work was partially supported by the Russian Foundation for Basic Research (grant 18-01-00074). Originally published in the Russian journal Teoriya Veroyatnostei i ee Primeneniya, 63 (2018), pp. 29–56. http://www.siam.org/journals/tvp/63-1/T98889.html †Sobolev Institute of Mathematics, Novosibirsk, Russia, and Novosibirsk State University, Novosibirsk, Russia (linke@math.nsc.ru, sibam@math.nsc.ru)

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In the paper, we propose a general approach to constructing explicit consistent estimators for some classes of nonlinear regression models. These estimators can be used as initial ones in one-step estimation procedures capable of delivering, in a sense, optimal estimators in an explicit form.

AB - In the paper, we propose a general approach to constructing explicit consistent estimators for some classes of nonlinear regression models. These estimators can be used as initial ones in one-step estimation procedures capable of delivering, in a sense, optimal estimators in an explicit form.

KW - Asymptotic normality

KW - Explicit estimator

KW - Initial estimator

KW - Nonlinear regression

KW - One-step estimator

KW - α -consistency

UR - http://www.scopus.com/inward/record.url?scp=85057338140&partnerID=8YFLogxK

U2 - 10.1137/S0040585X97T988897

DO - 10.1137/S0040585X97T988897

M3 - Article

AN - SCOPUS:85057338140

VL - 63

SP - 22

EP - 44

JO - Theory of Probability and its Applications

JF - Theory of Probability and its Applications

SN - 0040-585X

IS - 1

ER -