Results 31 to 40 of about 83,889,227 (308)

Structural Measurement Errors in Nonseparable Models [PDF]

open access: yes, 2009
This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly.
Winter, Joachim, Hoderlein, Stefan
core   +2 more sources

Errors in Variables in Linear Systems [PDF]

open access: yesEconometrica, 1987
This paper extends the simple errors-in-variables bound to the setting of systems of equations. Both diagonal and nondiagonal measurement error covariance matrices are considered. In the nondiagonal case, the analogue of the simple errors-in-variables interval of estimates is an ellipsoid with diagonal equal to the line segment connecting the direct ...
openaire   +2 more sources

SPECIFICATION TESTING FOR ERRORS-IN-VARIABLES MODELS [PDF]

open access: yesEconometric Theory, 2020
This paper considers specification testing for regression models with errors-in-variables and proposes a test statistic comparing the distance between the parametric and nonparametric fits based on deconvolution techniques. In contrast to the methods proposed by Hall and Ma (2007, Annals of Statistics, 35, 2620–2638) and Song (2008, Journal of ...
Otsu, Taisuke, Taylor, Luke Nicholas
openaire   +2 more sources

Solution for a time-series AR model based on robust TLS estimation

open access: yesGeomatics, Natural Hazards & Risk, 2019
We discuss an algorithm for the autoregression (AR) model as a typical time-series model. By analyzing the structure of the AR model, we highlight the shortcomings of traditional algorithms for model parameter estimation and propose an approach to ...
Yeqing Tao, Qiaoning He, Yifei Yao
doaj   +1 more source

Testing straightness of line objects using total least squares [PDF]

open access: yesTehnika, 2017
The paper presents the adaptation (fitting) of a set of points, with an estimated two-dimensional positions, to the straight line model of the by the application of the Weighted Total Least Squares, WTLS.
Popović Jovan   +4 more
doaj   +1 more source

An Overview of Linear Structural Models in Errors in Variables Regression

open access: yesRevstat Statistical Journal, 2010
This paper aims to overview the numerous approaches that have been developed to estimate the parameters of the linear structural model. The linear structural model is an example of an errors in variables model, or measurement error model that has wide ...
Jonathan Gillard
doaj   +1 more source

Errors-in-Variables Models [PDF]

open access: yes, 2000
Errors-in-variables (EIV) models axe regression models in which the regres-sors axe observed with errors. These models include the linear EIV models, the nonlinear EIV models, and the partially linear EIV models. Suppose that we want to investigate the relationship between the yield (Y) of corn and available nitrogen (X) in the soil.
openaire   +3 more sources

Empirical Likelihood Confidence Region for Parameters in Semi-linear Errors-in-Variables Models [PDF]

open access: yes, 2006
This paper proposes a constrained empirical likelihood confidence region for a parameter in the semi-linear errors-in-variables model. The confidence region is constructed by combining the score function corresponding to the squared orthogonal distance
Kong, Efang, Cui, Hengjian
core   +1 more source

Nonparametric Regression with Errors in Variables

open access: yesThe Annals of Statistics, 1993
The effect of errors in variables in nonparametric regression estimation is examined. To account for errors in covariates, deconvolution is involved in the construction of a new class of kernel estimators. It is shown that optimal local and global rates of convergence of these kernel estimators can be characterized by the tail behavior of the ...
Fan, Jianqing, Truong, Young K.
openaire   +2 more sources

Asymptotic normality of total least squares estimator in a multivariate errors-in-variables model AX=B

open access: yesModern Stochastics: Theory and Applications, 2016
We consider a multivariate functional measurement error model $AX\approx B$. The errors in $[A,B]$ are uncorrelated, row-wise independent, and have equal (unknown) variances.
Alexander Kukush   +1 more
doaj   +1 more source

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