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Title:

The Classical Linear Regression Model with one Incomplete Binary Variable

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We present three different methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to o...

We present three different methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi imputation, single imputation, multiple imputation, modified first order regression. After a brief theoretical description of the simulation experiment, MSE-ratio, variance and bias are used to illustrate differences within and between the approaches. Key words: binary variables; imputation; incomplete data; logistic regression; simulation experiment; 1 Introduction Statistical analysis with incomplete data is a common problem in practice. The linear regression as a main tool therefore often is affected by missing val. 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/paper178.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper178.ps.Z Minimize

Document Type:

text

Language:

en

Subjects:

regression ; simulation

regression ; simulation Minimize

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310 Collections of general statistics *(computed)*

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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 Classical Linear Regression Model with one Incomplete Binary Variable

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Description:

We present three di erent methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are con ned to one...

We present three di erent methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are con ned to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi imputation, single imputation, multiple imputation, modi ed rst order regression. After a brief theoretical description of the simulation experiment, MSE-ratio, variance and bias are used to illustrate di erences within and between the approaches. Key words: binary variables � imputation � incomplete data � logistic regression � simulation experiment� 1 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-08-15

Source:

http://epub.ub.uni-muenchen.de/1566/1/paper_178.pdf

http://epub.ub.uni-muenchen.de/1566/1/paper_178.pdf 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:

The Additive Model with Missing Values in the Independent Variable - Theory and Simulation

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After a short introduction of the model, the missing mechanism and the method of inference some imputation procedures are introduced with special focus on the simulation experiment. Within this experiment, the simple additive model y = f(x) + e is assumed to have missing values in the independent variable according to MCAR. Besides the well-know...

After a short introduction of the model, the missing mechanism and the method of inference some imputation procedures are introduced with special focus on the simulation experiment. Within this experiment, the simple additive model y = f(x) + e is assumed to have missing values in the independent variable according to MCAR. Besides the well-known complete case analysis, mean imputation plus random noise, a single imputation and two ways of nearest neighbor imputation are used. These methods are compared within a simulation experiment based on the average mean square error, variances and biases of \hat{f}(x) at the knots. Minimize

Year of Publication:

2002-01-01

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/1653/1/paper_272.pdf ; Nittner, T. (2002): The Additive Model with Missing Values in the Independent Variable - Theory and Simulation. Sonderforschungsbereich 386, Discussion Paper 272

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Title:

Missing at Random (MAR) in Nonparametric Regression - A Simulation Experiment

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This paper considers an additive model y = f(x) + e when some observations on x are missing at random but corresponding observations on y are available. Especially for this model missing at random is an interesting case because of the fact that the complete case analysis is not expected to be suitable. A simulation study is reported and methods ...

This paper considers an additive model y = f(x) + e when some observations on x are missing at random but corresponding observations on y are available. Especially for this model missing at random is an interesting case because of the fact that the complete case analysis is not expected to be suitable. A simulation study is reported and methods are compared based on superiority measures as the sample mean squared error, sample variance and estimated sample bias. In detail, complete case analysis, zero order regression plus random noise, single imputation and nearest neighbor imputation are discussed. Minimize

Year of Publication:

2002-01-01

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/1662/1/paper_284.pdf ; Nittner, T. (2002): Missing at Random (MAR) in Nonparametric Regression - A Simulation Experiment. Sonderforschungsbereich 386, Discussion Paper 284

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Title:

The Classical Linear Regression Model with one Incomplete Binary Variable

Description:

We present three different methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to o...

We present three different methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi imputation, single imputation, multiple imputation, modified first order regression. After a brief theoretical description of the simulation experiment, MSE-ratio, variance and bias are used to illustrate differences within and between the approaches. Minimize

Year of Publication:

1999-01-01

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/1566/1/paper_178.pdf ; Toutenburg, Helge und Nittner, T. (1999): The Classical Linear Regression Model with one Incomplete Binary Variable. Sonderforschungsbereich 386, Discussion Paper 178

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Title:

Estimating A Polynomial Regression With Measurement Errors In The Structural And In The Functional Case - A Comparison

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Two methods of estimating the parameters of a polynomial regression with measurement errors in the regressor variable are compared to each other with respect to their relative efficiency and robustness. One of the two estimators (SLS) is valid for the structural variant of the model and uses the assumption that the true regressor variable is nor...

Two methods of estimating the parameters of a polynomial regression with measurement errors in the regressor variable are compared to each other with respect to their relative efficiency and robustness. One of the two estimators (SLS) is valid for the structural variant of the model and uses the assumption that the true regressor variable is normally distributed, while the other one (ALS and also its small sample modification MALS) does not need any assumption on the regressor distribution. SLS turns out to react rather strongly on violations of the normality assumption as far as its bias is concerned but is quite robust with respect to its MSE. It is more efficient than ALS or MALS whenever the normality assumption holds true. Minimize

Year of Publication:

2000-01-01

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/1587/1/paper_197.pdf ; Schneeweiß, Hans und Nittner, T. (2000): Estimating A Polynomial Regression With Measurement Errors In The Structural And In The Functional Case - A Comparison. Sonderforschungsbereich 386, Discussion Paper 197

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Title:

Identifying Missing Data Mechanisms in (2 x 2)-Contingency Tables

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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 tabl...

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

Year of Publication:

2004-01-01

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/1744/1/paper_373.pdf ; Nittner, T. und Toutenburg, Helge (2004): Identifying Missing Data Mechanisms in (2 x 2)-Contingency Tables. Sonderforschungsbereich 386, Discussion Paper 373

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Title:

Statistische Methoden bei unvollständigen Daten

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Dieser Artikel gibt einen überblick über die Problematik fehlender Daten im Rahmen der statistischen Datenanalyse. Im Prinzip sollte er auch Lesern mit geringem mathematischen und statistischen Wissen dienlich sein und sie mathematisch nicht überfordern.Gegebenenfalls kann über allzu theoretische Komponenten hinweggelesen werden.

Dieser Artikel gibt einen überblick über die Problematik fehlender Daten im Rahmen der statistischen Datenanalyse. Im Prinzip sollte er auch Lesern mit geringem mathematischen und statistischen Wissen dienlich sein und sie mathematisch nicht überfordern.Gegebenenfalls kann über allzu theoretische Komponenten hinweggelesen werden. Minimize

Year of Publication:

2004-01-01

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/1750/1/paper_380.pdf ; Toutenburg, Helge und Heumann, Christian und Nittner, T. (2004): Statistische Methoden bei unvollständigen Daten. Sonderforschungsbereich 386, Discussion Paper 380

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Title:

Parametric and Nonparametric Regression with Missing X's - A Review

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This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoretical basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduc...

This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoretical basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduction to missing data within regression analysis and as an extension to the early paper of Little (1992). Minimize

Year of Publication:

2002-01-01

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/1664/1/paper_286.pdf ; Toutenburg, Helge und Heumann, Christian und Nittner, T. und Scheid, S. (2002): Parametric and Nonparametric Regression with Missing X's - A Review. Sonderforschungsbereich 386, Discussion Paper 286

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Title:

An integrative genomics screen uncovers ncRNA T-UCR functions in neuroblastoma tumours

Author:

Mestdagh, P; JFA; CORA; Fredlund, E; Pattyn, Frédéric; U0071729; Rihani, A; Van Maerken, T; Vermeulen, J; Kumps, C; Menten, B; De Preter, K; Schramm, A; Schulte, J; Noguera, R; Schleiermacher, G; Janoueix-Lerosey, I; Laureys, G; Powel, R; Nittner, David; U0063864; Marine, Chris; U0063154; Ringnér, M; Speleman, F; Vandesompele, J; JLA Minimize authors

Description:

Different classes of non-coding RNAs, including microRNAs, have recently been implicated in the process of tumourigenesis. In this study, we examined the expression and putative functions of a novel class of non-coding RNAs known as transcribed ultraconserved regions (T-UCRs) in neuroblastoma. Genome-wide expression profiling revealed correlatio...

Different classes of non-coding RNAs, including microRNAs, have recently been implicated in the process of tumourigenesis. In this study, we examined the expression and putative functions of a novel class of non-coding RNAs known as transcribed ultraconserved regions (T-UCRs) in neuroblastoma. Genome-wide expression profiling revealed correlations between specific T-UCR expression levels and important clinicogenetic parameters such as MYCN amplification status. A functional genomics approach based on the integration of multi-level transcriptome data was adapted to gain insights into T-UCR functions. Assignments of T-UCRs to cellular processes such as TP53 response, differentiation and proliferation were verified using various cellular model systems. For the first time, our results define a T-UCR expression landscape in neuroblastoma and suggest widespread T-UCR involvement in diverse cellular processes that are deregulated in the process of tumourigenesis. ; status: published Minimize

Publisher:

Scientific & Medical Division, Macmillan Press

Year of Publication:

2010-06

Document Type:

Description (Metadata) only ; IT ; article

Language:

en

Subjects:

Cell Line ; Tumor ; Conserved Sequence ; Gene Expression Profiling ; Gene Expression Regulation ; Neoplastic ; Genomics ; Histones ; Humans ; Neuroblastoma ; Prognosis ; RNA ; Messenger ; RNA ; Neoplasm ; RNA ; Untranslated ; Reproducibility of Results ; Transcription ; Genetic

Cell Line ; Tumor ; Conserved Sequence ; Gene Expression Profiling ; Gene Expression Regulation ; Neoplastic ; Genomics ; Histones ; Humans ; Neuroblastoma ; Prognosis ; RNA ; Messenger ; RNA ; Neoplasm ; RNA ; Untranslated ; Reproducibility of Results ; Transcription ; Genetic Minimize

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Oncogene vol:29 issue:24 pages:3583-3592

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