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Title:

Non-Existence of the First Moment of the Adjusted Least Squares Estimator in Multivariate Errors-in-Variables Model

Description:

Adjusted least squares, Equation error model, Functional model, Infinite first moment, Linear multivariate error-in-variables model, Structural model, 62J05, 62H12, 62H10

Adjusted least squares, Equation error model, Functional model, Infinite first moment, Linear multivariate error-in-variables model, Structural model, 62J05, 62H12, 62H10 Minimize

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Title:

A small sample estimator for a polynomial regression with errors in the variables

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article

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Title:

The Invariance of Some Score Tests in the Linear Model With Classical Measurement Error

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article

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Title:

Note on Two Estimators for the Polynomial Regression with Errors in the Variables

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-09-16

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper96.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper96.ps.Z Minimize

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text

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en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

The Invariance of Score Tests to Measurement Error

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. This paper investigates the invariance property of score tests for assessing heteroscedasticity, first-order autoregressive disturbance, and the need for a Box-Cox power transformation. Under specific constraints, we show that the score tests for measurement error models are the same as the corresponding well-established tests derived from cla...

. This paper investigates the invariance property of score tests for assessing heteroscedasticity, first-order autoregressive disturbance, and the need for a Box-Cox power transformation. Under specific constraints, we show that the score tests for measurement error models are the same as the corresponding well-established tests derived from classical regression models. We also discuss some possible generalizations. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-02-16

Source:

http://www.stat.sinica.edu.tw/library/c_tec_rep/c-2000-10.pdf

http://www.stat.sinica.edu.tw/library/c_tec_rep/c-2000-10.pdf Minimize

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text

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en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Robust linear regression via bounded influence M-estimators

Description:

We investigate optimal bounded influence M-estimators in the general normal regression model with respect to different sensitivities. As a result, we answer some open questions in F. R. Hampel et al. (Robust Statistics, Chap. 6, Wiley, New York). Moreover, we examine the relationship among different sensitives and their associated optimal estima...

We investigate optimal bounded influence M-estimators in the general normal regression model with respect to different sensitivities. As a result, we answer some open questions in F. R. Hampel et al. (Robust Statistics, Chap. 6, Wiley, New York). Moreover, we examine the relationship among different sensitives and their associated optimal estimators and extend the idea of change- of -variance sensitivity to the case of the predicted value. ; robust regression influence function sensitivity change-of-variance function Minimize

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Title:

Bias of the quasi score estimator of a measurement error model under misspecification of the regressor distribution

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In a structural error model the structural quasi score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribution as his mo...

In a structural error model the structural quasi score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribution as his model for the regressor distribution. In the first type of misspecification the true model consists of a mixture of normal distributions which cluster round a single normal distribution, in the second type the true distribution is a normal distribution admixed with a second normal distribution of low weight. In both cases of misspecification the bias, of course, tends to zero when the size of misspecification tends to zero. However, in the first case the bias goes to zero in a flat way so that small deviations from the true model lead to a negligible bias, whereas in the second case the bias is noticeable even for small deviations from the true model. Minimize

Publisher:

Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen München

Year of Publication:

2003

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310

ddc:310 Minimize

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http://www.econstor.eu/dspace/Nutzungsbedingungen

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 340

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Title:

On the Polynomial Measurement Error Model

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This paper discusses point estimation of the coefficients of polynomial measurement error (errors-in-variables) models. This includes functional and structural models. The connection between these models and total least squares (TLS) is also examined. A compendium of existing as well as new results is presented.

This paper discusses point estimation of the coefficients of polynomial measurement error (errors-in-variables) models. This includes functional and structural models. The connection between these models and total least squares (TLS) is also examined. A compendium of existing as well as new results is presented. Minimize

Year of Publication:

2001-01-01

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Subjects:

Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510

Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510 Minimize

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http://epub.ub.uni-muenchen.de/1639/1/paper_259.pdf ; Cheng, Chi-Lun und Schneeweiß, Hans (2001): On the Polynomial Measurement Error Model. Sonderforschungsbereich 386, Discussion Paper 259

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Title:

Bias of the Quasi Score Estimator of a Measurement Error Model Under Misspecification of the Regressor Distribution

Description:

In a structural error model the structural quasi score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribution as his mo...

In a structural error model the structural quasi score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribution as his model for the regressor distribution. In the first type of misspecification the true model consists of a mixture of normal distributions which cluster round a single normal distribution, in the second type the true distribution is a normal distribution admixed with a second normal distribution of low weight. In both cases of misspecification the bias, of course, tends to zero when the size of misspecification tends to zero. However, in the first case the bias goes to zero very fast so that small deviations from the true model lead only to a negligible bias, whereas in the second case the bias is noticeable even for small deviations from the true model. Minimize

Year of Publication:

2003-01-01

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Subjects:

Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510

Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510 Minimize

DDC:

519 Probabilities & applied mathematics *(computed)*

Relations:

http://epub.ub.uni-muenchen.de/1718/1/paper_340.pdf ; Schneeweiß, Hans und Cheng, Chi-Lun (2003): Bias of the Quasi Score Estimator of a Measurement Error Model Under Misspecification of the Regressor Distribution. Sonderforschungsbereich 386, Discussion Paper 340

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Title:

Bias of the structural quasi-score estimator of a measurement error model under misspecification of the regressor distribution

Description:

In a structural measurement error model the structural quasi-score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified, the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribut...

In a structural measurement error model the structural quasi-score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified, the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribution as his model for the regressor distribution. In the first type of misspecification, the true model consists of a mixture of normal distributions which cluster around a single normal distribution, in the second type, the true distribution is a normal distribution admixed with a second normal distribution of low weight. In both cases of misspecification, the bias, of course, tends to zero when the size of misspecification tends to zero. However, in the first case the bias goes to zero in a flat way so that small deviations from the true model lead to a negligible bias, whereas in the second case the bias is noticeable even for small deviations from the true model. ; Measurement error model Structural case Bias Misspecification Mixture of multivariate normals Quasi-score estimator Robustness Minimize

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