Asymptotics for weakly dependent errors-in-variables
Kybernetika, Tome 49 (2013) no. 5, pp. 692-704 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

Linear relations, containing measurement errors in input and output data, are taken into account in this paper. Parameters of these so-called errors-in-variables (EIV) models can be estimated by minimizing the total least squares (TLS) of the input-output disturbances. Such an estimate is highly non-linear. Moreover in some realistic situations, the errors cannot be considered as independent by nature. Weakly dependent ($\alpha$- and $\varphi$-mixing) disturbances, which are not necessarily stationary nor identically distributed, are considered in the EIV model. Asymptotic normality of the TLS estimate is proved under some reasonable stochastic assumptions on the errors. Derived asymptotic properties provide necessary basis for the validity of block-bootstrap procedures.
Linear relations, containing measurement errors in input and output data, are taken into account in this paper. Parameters of these so-called errors-in-variables (EIV) models can be estimated by minimizing the total least squares (TLS) of the input-output disturbances. Such an estimate is highly non-linear. Moreover in some realistic situations, the errors cannot be considered as independent by nature. Weakly dependent ($\alpha$- and $\varphi$-mixing) disturbances, which are not necessarily stationary nor identically distributed, are considered in the EIV model. Asymptotic normality of the TLS estimate is proved under some reasonable stochastic assumptions on the errors. Derived asymptotic properties provide necessary basis for the validity of block-bootstrap procedures.
Classification : 15A51, 15A52, 62E20, 62J05, 62J99, 65F15
Keywords: errors-in-variables (EIV); dependent errors; total least squares (TLS); asymptotic normality
@article{KYB_2013_49_5_a1,
     author = {Pe\v{s}ta, Michal},
     title = {Asymptotics for weakly dependent errors-in-variables},
     journal = {Kybernetika},
     pages = {692--704},
     year = {2013},
     volume = {49},
     number = {5},
     mrnumber = {3182634},
     zbl = {1278.15036},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_2013_49_5_a1/}
}
TY  - JOUR
AU  - Pešta, Michal
TI  - Asymptotics for weakly dependent errors-in-variables
JO  - Kybernetika
PY  - 2013
SP  - 692
EP  - 704
VL  - 49
IS  - 5
UR  - http://geodesic.mathdoc.fr/item/KYB_2013_49_5_a1/
LA  - en
ID  - KYB_2013_49_5_a1
ER  - 
%0 Journal Article
%A Pešta, Michal
%T Asymptotics for weakly dependent errors-in-variables
%J Kybernetika
%D 2013
%P 692-704
%V 49
%N 5
%U http://geodesic.mathdoc.fr/item/KYB_2013_49_5_a1/
%G en
%F KYB_2013_49_5_a1
Pešta, Michal. Asymptotics for weakly dependent errors-in-variables. Kybernetika, Tome 49 (2013) no. 5, pp. 692-704. http://geodesic.mathdoc.fr/item/KYB_2013_49_5_a1/

[1] Anderson, T. W.: An Introduction to Multivariate Statistical Analysis. John Wiley and Sons, New York 1958. | MR | Zbl

[2] Billingsley, P.: Convergence of Probability Measures. First edition. John Wiley and Sons, New York 1968. | MR

[3] Bradley, R. C.: Basic properties of strong mixing conditions. A survey and some open questions. Probab. Surveys 2 (2005), 107-144. | MR | Zbl

[4] Gallo, P. P.: Consistency of regression estimates when some variables are subject to error. Comm. Statist Theory Methods 11 (1982), 973-983. | DOI | MR | Zbl

[5] Gallo, P. P.: Properties of Estimators in Errors-in-Variables Models. Ph.D. Thesis, University of North Carolina, Chapel Hill 1982.

[6] Gleser, L. J.: Estimation in a multivariate ``errors in variables'' regression model: Large sample results. Ann. Statist. 9 (1981), 24-44. | DOI | MR | Zbl

[7] Golub, G. H., Loan, C. F. Van: An analysis of the total least squares problem. SIAM J. Numer. Anal. 17 (1980), 6, 883-893. | DOI | MR

[8] Healy, J. D.: Estimation and Tests for Unknown Linear Restrictions in Multivariate Linear Models. Ph.D. Thesis, Purdue University 1975. | MR

[9] Herrndorf, N.: A functional central limit theorem for strongly mixing sequence of random variables. Probab. Theory Rel. Fields 69 (1985), 4, 541-550. | MR

[10] Ibragimov, I. A., Linnik, Y. V.: Independent and Stationary Sequences of Random Variables. Wolters-Noordhoff 1971. | MR | Zbl

[11] Lin, Z., Lu, C.: Limit Theory for Mixing Dependent Random Variables. Springer-Verlag, New York 1997. | MR | Zbl

[12] Pešta, M.: Strongly consistent estimation in dependent errors-in-variables. Acta Univ. Carolin. - Math. Phys. 52 (2011), 1, 69-79. | MR | Zbl

[13] Pešta, M.: Total least squares and bootstrapping with application in calibration. Statistics: J. Theor. and Appl. Statistics 46 (2013), 5, 966-991. | DOI

[14] Rosenblatt, M.: Markov Processes: Structure and Asymptotic Behavior. Springer-Verlag, Berlin 1971. | MR | Zbl

[15] Utev, S. A.: The central limit theorem for $\varphi$-mixing arrays of random variables. Theory Prob. Appl. 35 (1990), 131-139. | DOI | MR