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Title:

Efficient estimation of population mean using incomplete survey data on study and auxiliary characteristics

Description:

This paper considers the problem of estimating the population mean using the ratio and product methods when some observations in the sample data are missing at random and the population mean of the auxiliary characteristic is not known. Besides an unbiased estimator arising from the total discard of incomplete pairs of observations, four general...

This paper considers the problem of estimating the population mean using the ratio and product methods when some observations in the sample data are missing at random and the population mean of the auxiliary characteristic is not known. Besides an unbiased estimator arising from the total discard of incomplete pairs of observations, four generally biased estimators are presented. The first two estimators arise form the partial utilization of data while the remaining two are based on full utilization. A comparative study of the efficiency properties of estimators is reported and the choice of estimators is discussed. Minimize

Publisher:

Dep. of Statistical Sciences "Paolo Fortunati", Universit di Bologna

Year of Publication:

2007-10-01T00:00:00Z

Source:

Statistica, Vol 63, Iss 2, Pp 223-236 (2007)

Statistica, Vol 63, Iss 2, Pp 223-236 (2007) Minimize

Document Type:

article

Language:

English ; Italian

Subjects:

LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q

LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q Minimize

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http://rivista-statistica.unibo.it/article/view/350

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Title:

Efficient estimation of population mean using incomplete survey data on study and auxiliary characteristics

Description:

This paper considers the problem of estimating the population mean using the ratio and product methods when some observations in the sample data are missing at random and the population mean of the auxiliary characteristic is not known. Besides an unbiased estimator arising from the total discard of incomplete pairs of observations, four general...

This paper considers the problem of estimating the population mean using the ratio and product methods when some observations in the sample data are missing at random and the population mean of the auxiliary characteristic is not known. Besides an unbiased estimator arising from the total discard of incomplete pairs of observations, four generally biased estimators are presented. The first two estimators arise form the partial utilization of data while the remaining two are based on full utilization. A comparative study of the efficiency properties of estimators is reported and the choice of estimators is discussed. Minimize

Publisher:

Dep. of Statistical Sciences "Paolo Fortunati", Universit di Bologna

Year of Publication:

2007-10-01T00:00:00Z

Source:

Statistica, Vol 63, Iss 2, Pp 223-236 (2007)

Statistica, Vol 63, Iss 2, Pp 223-236 (2007) Minimize

Document Type:

article

Language:

English ; Italian

Subjects:

LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q

LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q Minimize

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Title:

The Impact of Missing Values on the Reliability Measures in a Linear Model

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Reliability measures in linear models are used in geodetic science and elsewhere to quantify the potential to detect outliers and to suppress their impact on the regression estimates. Here we shall study the effect of missing values on these reliability measures with the idea that, under a proper design, they should not change drastically when s...

Reliability measures in linear models are used in geodetic science and elsewhere to quantify the potential to detect outliers and to suppress their impact on the regression estimates. Here we shall study the effect of missing values on these reliability measures with the idea that, under a proper design, they should not change drastically when such a situation occurs. 1 Introduction Since Baarda (1976) defined reliability measures to quantify the potential to detect outliers in a linear model, this technique has found wide applications in geodesy, photogrammetry, mapping and related areas. Generalizations to include the case of correlated measurements have been proposed quite recently by Schaffrin (1997). It is still unclear, however, what the effect of missing values would be on these reliability measures. This will be studied in more detail in this contribution, thereby relying on the previous studies of Toutenburg, Heumann, Fieger and Park (1995) and Toutenburg, Fieger and Heumann . Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper125.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper125.ps.Z Minimize

Document Type:

text

Language:

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:

Role of Categorical Variables in Multicollinearity in Linear Regression Model

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The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory vari-ables and analyzes how the dummy variables and choice of reference category can affect the degree of multicoll...

The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory vari-ables and analyzes how the dummy variables and choice of reference category can affect the degree of multicollinearity. Such an effect is analyzed analytically as well as numerically through simulation and real data application. Key Words: Linear regression model, multicollinearity, dummy variable, condition number 1 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-14

Source:

http://epub.ub.uni-muenchen.de/2081/1/report008_statistics.pdf

http://epub.ub.uni-muenchen.de/2081/1/report008_statistics.pdf Minimize

Document Type:

text

Language:

en

Rights:

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:

Linear Models: Least Squares and Alternatives, Second Edition

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The book is based on several years of experience of both authors in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights...

The book is based on several years of experience of both authors in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results about the definiteness of matrices, especially for the differences of matrices, which enable superiority comparisons of two biased estimates to be made for the first time. We have attempted to provide a unified theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2014-12-05

Source:

http://staff.ulsu.ru/semushin/_index/_pilocus/_gist/docs/mycourseware/15-numeth=ised/2-reading/pdf-s/other_books/Rao C.R., H.Toutenberg. Linear Models. Least Squares and Al.pdf

http://staff.ulsu.ru/semushin/_index/_pilocus/_gist/docs/mycourseware/15-numeth=ised/2-reading/pdf-s/other_books/Rao C.R., H.Toutenberg. Linear Models. Least Squares and Al.pdf Minimize

Document Type:

text

Language:

en

Rights:

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:

Identifying missing data mechanisms in (2 x 2)-contingency tables

Description:

Consider the sample of two binary variables X and Y with some missing structure within X or Y. The knowledge about the corresponding values of the observed covariate allows to play through all possible originally' complete data sets. After defining the notation, including some theoretical work, a test for non MCAR within the complete case table ...

Consider the sample of two binary variables X and Y with some missing structure within X or Y. The knowledge about the corresponding values of the observed covariate allows to play through all possible originally' complete data sets. After defining the notation, including some theoretical work, a test for non MCAR within the complete case table is presented. Simulating all possible tables enables some testing on non MAR. A simulation experiment is used to illustrate this context. Minimize

Publisher:

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

Year of Publication:

2004

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310 ; missing data mechanism : odds-ratio ; simulation experiment ; testing non MAR

ddc:310 ; missing data mechanism : odds-ratio ; simulation experiment ; testing non MAR Minimize

Rights:

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

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 373

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Title:

On the regression method of estimation of population mean from incomplete survey data through imputation

Author:

Description:

When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation. The performance properties of the estimators based on the methods of mean imputation, regression imputation and no imputation are analyzed and the superiority ...

When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation. The performance properties of the estimators based on the methods of mean imputation, regression imputation and no imputation are analyzed and the superiority of one method over the other is examined. Minimize

Publisher:

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

Year of Publication:

2005

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310 ; missing data mechanism ; regression analysis ; generalized additive models ; imputation ; MSE-superiority

ddc:310 ; missing data mechanism ; regression analysis ; generalized additive models ; imputation ; MSE-superiority Minimize

Rights:

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

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 442

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Title:

Consequences of departure from normality on the properties of calibration estimators

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Description:

This paper considers the classical and inverse calibration estimators and discusses the consequences of departure from normality of errors on their bias and mean squared error properties when the errors in calibration process are small.

This paper considers the classical and inverse calibration estimators and discusses the consequences of departure from normality of errors on their bias and mean squared error properties when the errors in calibration process are small. Minimize

Publisher:

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

Year of Publication:

2005

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310

ddc:310 Minimize

Rights:

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

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

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 441

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Title:

Risk performance of Stein-rule estimators over the least squares estimators of regression coefficients under quadratic loss structures

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Description:

This paper presents a general loss function under quadratic loss structure and discusses the comparison of risk functions associated with the unbiased least squares and biased Stein-rule estimators of the coefficients in a linear regression model.

This paper presents a general loss function under quadratic loss structure and discusses the comparison of risk functions associated with the unbiased least squares and biased Stein-rule estimators of the coefficients in a linear regression model. Minimize

Publisher:

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

Year of Publication:

2006

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310

ddc:310 Minimize

Rights:

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

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 495

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Title:

Performance of double k-class estimators for coefficients in linear regression models with non spherical disturbances under asymmetric losses

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Description:

The risk of the family of feasible generalized double k-class estimators under LINEX loss function is derived in a linear regression model. The disturbances are assumed to be non-spherical and their variance covariance matrix is unknown.

The risk of the family of feasible generalized double k-class estimators under LINEX loss function is derived in a linear regression model. The disturbances are assumed to be non-spherical and their variance covariance matrix is unknown. Minimize

Publisher:

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

Year of Publication:

2006

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310

ddc:310 Minimize

Rights:

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

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

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 509

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