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Title:

Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates

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This paper presents methods to analyze and detect non-MCAR processes that lead to missing covariate values in linear regression models. First, the data situation and the problem is sketched. The next section provides an overview of the methods that deal with missing covariate values. The idea of using outlier methods to detect non-MCAR processes...

This paper presents methods to analyze and detect non-MCAR processes that lead to missing covariate values in linear regression models. First, the data situation and the problem is sketched. The next section provides an overview of the methods that deal with missing covariate values. The idea of using outlier methods to detect non-MCAR processes is described in section 3. Section 4 uses these ideas to introduce a graphical method to visualize the problem. Possible extensions conclude the presentation. 1 Data and problem We consider the classical linear regression model y(n \Theta 1) = X(n \Theta p) fi(p \Theta 1) + ffl(n \Theta 1) with missing data in the n \Theta p covariate matrix X . Reorganization of the n rows of the data matrix X , the corresponding elements of the response y and the error term ffl leads to the following structure ` y c y mis ' = ` X c X mis ' fi + ` ffl c ffl mis ' (1) The index c indicates the completely observed submodel whereas the index mis . Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper204.ps.Z

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text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Title:

Shrinkage Estimation Of Incomplete Regression Models By Yates Procedure

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The problem of estimating the coefficients in a linear regression model is considered when some of the response values are missing. The conventional Yates procedure employing least squares predictions for missing values does not lead to any improvement over the least squares estimator using complete observations only. However, if we use Stein-ru...

The problem of estimating the coefficients in a linear regression model is considered when some of the response values are missing. The conventional Yates procedure employing least squares predictions for missing values does not lead to any improvement over the least squares estimator using complete observations only. However, if we use Stein-rule predictions, it is demonstrated that some improvement can be achieved. An unbiased estimator of the mean squared error matrix of the proposed estimator of coefficient vector is also presented. Some work on the application of the proposed estimation procedure to real-world data sets involving some discrete variables in the set of explanatory variables is under way and will be reported in future. 1 Introduction More than six decades ago, Yates (1933) has presented a procedure for the estimation of parameters when an incomplete data set due to some missing observations is available for the purpose of statistical analysis. The procedure essentia. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper69.ps.Z

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Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Title:

Towards QoS-support in the Presence of Handover

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This paper copes with QoS support within the core network. It is assumed that due to the mobility of mobile stations frequently congestion situations occur at different links within the core network. Mechanisms to cope with these temporary congestion situations are proposed within this extended abstract. Even in the case of handovers between dif...

This paper copes with QoS support within the core network. It is assumed that due to the mobility of mobile stations frequently congestion situations occur at different links within the core network. Mechanisms to cope with these temporary congestion situations are proposed within this extended abstract. Even in the case of handovers between different access networks (interworking handover) these mechanisms should be applicable to realize proper QoS support. The key idea of our approach is to rely on prioritization mechanisms to cope with overload situations within the core network, that is assumed to be a DiffServ capable IP-based network. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

http://www.comcar.de/IQWiM99/submissions/fieger.pdf

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Document Type:

text

Language:

en

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Title:

Estimation of Parameters in Multiple Regression With Missing X-Observations using Modified First Order Regression Procedure

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This paper considers the estimation of coefficients in a linear regression model with missing observations in the independent variables and introduces a modification of the standard first order regression method for imputation of missing values. The modification provides stochastic values for imputation. Asymptotic properties of the estimators f...

This paper considers the estimation of coefficients in a linear regression model with missing observations in the independent variables and introduces a modification of the standard first order regression method for imputation of missing values. The modification provides stochastic values for imputation. Asymptotic properties of the estimators for the regression coefficients arising from the proposed modification are derived when either both the number of complete observations and the number of missing values grow large or only the number of complete observations grows large and the number of missing observations stays fixed. Using these results, the proposed procedure is compared with two popular procedures---one which utilizes only the complete observations and the other which employs the standard first order regression imputation method for missing values. It is suggested that an elaborate simulation experiment will be helpful to evaluate the gain in efficiency especially in case of. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper38.ps.Z

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Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)* ; 519 Probabilities & applied mathematics *(computed)*

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Title:

Weighted Modified First Order Regression Procedures for Estimation in Linear Models with Missing X-Observations

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This paper considers the estimation of coefficients in a linear regression model with missing observations in the independent variables and introduces a modification of the standard first order regression method for imputation of missing values. The modification provides stochastic values for imputation and, as an extension, makes use of the pri...

This paper considers the estimation of coefficients in a linear regression model with missing observations in the independent variables and introduces a modification of the standard first order regression method for imputation of missing values. The modification provides stochastic values for imputation and, as an extension, makes use of the principle of weighted mixed regression. The proposed procedures are compared with two popular procedures---one which utilizes only the complete observations and the other which employs the standard first order regression imputation method for missing values. A simulation experiment to evaluate the gain in efficiency and to examine interesting issues like the impact of varying degree of multicollinearity in explanatory variables is proceeded. Some work on the case of discrete regressor variables is in progress and will be reported in a future article to follow. 1 Introduction It is not uncommon in many applications of the regression analysis that s. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper127.ps.Z

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Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Title:

New features in MAREG 0.2.0

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This paper describes changes in the software tool MAREG from version 0.0.7 (July 1997) to version 0.2.0 (June 1999). As these new features are not implemented in WinMAREG yet, they can only be used via editing the ini-files (*.cai). Handling and features of Version 0.0.7 are described in Fieger, Heumann and Kastner (1996), Fieger, Kastner and He...

This paper describes changes in the software tool MAREG from version 0.0.7 (July 1997) to version 0.2.0 (June 1999). As these new features are not implemented in WinMAREG yet, they can only be used via editing the ini-files (*.cai). Handling and features of Version 0.0.7 are described in Fieger, Heumann and Kastner (1996), Fieger, Kastner and Heumann (1998) and Kastner, Fieger and Heumann (1997). 1 Changes 1 1.1 Test statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 GEE output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 New general options 2 2.1 Expected values of the response variable . . . . . . . . . . . . . . . . . 2 2.2 Global Wald tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Specifying start values . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 Sequential logit link . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 New options for the GEE module 4 4 New values for .cai. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper156.ps.Z

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Document Type:

text

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en

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Title:

Approximate Confidence Regions for Minimax-Linear Estimators

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Minimax estimation is based on the idea, that the quadratic risk function for the estimate fi is not minimized over the entire parameter space IR K , but only over an area B(fi) that is restricted by a priori knowledge. If all restrictions define a convex area, this area can often be enclosed in an ellipsoid of the form B(fi) = ffi : fi 0 T fi r...

Minimax estimation is based on the idea, that the quadratic risk function for the estimate fi is not minimized over the entire parameter space IR K , but only over an area B(fi) that is restricted by a priori knowledge. If all restrictions define a convex area, this area can often be enclosed in an ellipsoid of the form B(fi) = ffi : fi 0 T fi rg. The ellipsoid has a larger volume than the cuboid. Hence, the transition to an ellipsoid as a priori information represents a weakening, but comes with an easier mathematical handling. Deriving the linear Minimax estimator we see that it is biased and nonoperationable. Using an approximation of the non-central 2 -distribution and prior information on the variance, we get an operationable solution which is compared with OLSE with respect to the size of the corresponding confidence intervals. 1 Introduction We consider the linear regression model y = Xfi + ffl; ffl N(0; oe 2 I) (1) with nonstochastic regressor matrix X of full co. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper166.ps.Z

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text

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en

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Title:

Regression Modelling with Fixed Effects - Missing Values and related Problems

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The paper considers new devices to predict the response variable using a convex target function weighting the response and its expectation. A MDEP-matrix superiority condition is given concerning BLUE, RLSE and mixed estimator where the latter is used in case of imputation for missing values. A small simulation study compares the alternative est...

The paper considers new devices to predict the response variable using a convex target function weighting the response and its expectation. A MDEP-matrix superiority condition is given concerning BLUE, RLSE and mixed estimator where the latter is used in case of imputation for missing values. A small simulation study compares the alternative estimators. Finally the detection of non-MCAR processes in linear regression is discussed. Some Hypotheses about Statistics for the twenty first century 1. Statistical research in next century will be largely based on artificial intelligence. Appropriate models for any given data set will be searched through computers. 2. We have many empirical studies now. These will be fruitfully employed in developing non-conjugate prior distributions that will describe reality. The availability of realistic prior distributions will lead to high importance to Bayesian inference. 3. Generally we pay attention to one problem at a time. Next century will provide a. Minimize

Publisher:

Century, Springer

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper123.ps.Z

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Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Title:

WinMAREG Quick Start

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This paper is a short introduction into the usage of WinMAREG. Two examples are used to illustrate the most common options and features of the software. Keywords: User guide, MAREG, WinMAREG. 1 Introduction This quick start will give the user a short introduction into the handling of WinMAREG. We present the analysis of two data sets, which are ...

This paper is a short introduction into the usage of WinMAREG. Two examples are used to illustrate the most common options and features of the software. Keywords: User guide, MAREG, WinMAREG. 1 Introduction This quick start will give the user a short introduction into the handling of WinMAREG. We present the analysis of two data sets, which are provided with the software. These are the `Respiratory Disorder Data' described in Miller, Davis and Landis (1993) and the `Ohio Children Data' described e. g. in Fitzmaurice and Laird (1993). Most of the features and options are presented. A detailled description can be found in the (context sensitive) online help or in Fieger, Heumann and Kastner (1996). Starting the program displays the main window of WinMAREG presented in figure 1. The menubar consists of the usual file menu (opening and closing data files), the options menu, the two statistics menues (ML and GEE1) and a help menu. The options menu provides entrys that determine the appeara. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-12

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper108.ps.Z

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text

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en

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Title:

C++ Klassen zur Linearen Regression bei fehlenden Kovariablen

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In diesem Bericht werden C++ Klassen zu linearen Modellen mit fehlenden Werten in der Kovariablenmatrix X vorgestellt. Diese Klassen implementieren erste verwendbare Modelle wie Zero Order Regression, First Order Regression oder modified First Order Regression und dienen als Ausgangsbasis für weitere Modellklassen. Die hier vorgestellten Klassen...

In diesem Bericht werden C++ Klassen zu linearen Modellen mit fehlenden Werten in der Kovariablenmatrix X vorgestellt. Diese Klassen implementieren erste verwendbare Modelle wie Zero Order Regression, First Order Regression oder modified First Order Regression und dienen als Ausgangsbasis für weitere Modellklassen. Die hier vorgestellten Klassen können in Simulationsstudien oder für konkrete Datensätze verwendet werden. Minimize

Year of Publication:

1997-01-01

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Subjects:

Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510

Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510 Minimize

Relations:

http://epub.ub.uni-muenchen.de/1455/1/paper_61.pdf ; Fieger, A. (1997): C++ Klassen zur Linearen Regression bei fehlenden Kovariablen. Sonderforschungsbereich 386, Discussion Paper 61

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