Loading
Error: Cannot Load Popup Box
Skip to hit list
Adjust your hit list
Further result pages
Mobile

A
A
A

A

English
Deutsch
Français
Español
Polski
Ελληνικά
Українська
中文
 Logged in as

Log Out

Login
BASIC
SEARCH
ADVANCED
SEARCH
HELP
BROWSING
SEARCH
HISTORY
Your search
Search For:
Entire Document
Title
Author
Subject
Boost open access documents
Find
Linguistics tools
Verbatim search
Additional word forms
Multilingual synonyms
Statistics
11 hits
in 72,045,933 documents
in 0.24 seconds
Please leave the following field blank:
Home
»
Search: Markus Thamerus
Hit List
Hit list
1.
A small sample estimator for a polynomial regression with errors in the variables
Title:
A small sample estimator for a polynomial regression with errors in the variables
Author:
ChiLun Cheng
;
Hans Schneeweiss
;
Markus Thamerus
ChiLun Cheng
;
Hans Schneeweiss
;
Markus Thamerus
Minimize authors
Document Type:
article
URL:
http://www.blackwellsynergy.com/doi/abs/10.1111/14679868.00258
http://www.blackwellsynergy.com/doi/abs/10.1111/14679868.00258
Minimize
Content Provider:
RePEc: Research Papers in Economics
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
2.
Extreme value analysis of Munich air pollution data
Open Access
Title:
Extreme value analysis of Munich air pollution data
Author:
Helmut Küchenhoff
;
Markus Thamerus
Helmut Küchenhoff
;
Markus Thamerus
Minimize authors
Description:
We present three different approaches to model extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data the parameters of the generalized Pareto distribution were estimated. Accounting for aut...
We present three different approaches to model extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation clusters of exceedances were used. To get information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of O 3 and of NO 2 at two monitoring sites within the city of Munich. Keywords: air pollution, extreme values, generalized extreme value distribution, generalized Pareto distribution, logistic regression. 1 Introduction Peak concentrations of single air pollutants are of particular interest in the determination of the air quality. Extreme concentrations of harmful atmosph.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090414
Source:
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper04.ps.Z
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper04.ps.Z
Minimize
Document Type:
text
Language:
en
Subjects:
air pollution ; extreme values ; generalized extreme value distribution ; generalized Pareto distribution ; logistic regression
air pollution ; extreme values ; generalized extreme value distribution ; generalized Pareto distribution ; logistic regression
Minimize
DDC:
333 Economics of land & energy
(computed)
;
310 Collections of general statistics
(computed)
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
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.5209
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.5209
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
3.
Modelling Count Data with Heteroscedastic Measurement Error in the Covariates
Open Access
Title:
Modelling Count Data with Heteroscedastic Measurement Error in the Covariates
Author:
Markus Thamerus
Markus Thamerus
Minimize authors
Description:
This paper is concerned with the estimation of the regression coefficients for a count data model when one of the explanatory variables is subject to heteroscedastic measurement error. The observed values W are related to the true regressor X by the additive error model W=X+U. The errors U are assumed to be normally distributed with zero mean bu...
This paper is concerned with the estimation of the regression coefficients for a count data model when one of the explanatory variables is subject to heteroscedastic measurement error. The observed values W are related to the true regressor X by the additive error model W=X+U. The errors U are assumed to be normally distributed with zero mean but heteroscedastic variances, which are known or can be estimated from repeated measurements. Inference is done by using quasi likelihood methods, where a model of the observed data is specified only through a mean and a variance function for the response Y given W and other correctly observed covariates. Although this approach weakens the assumption of a parametric regression model, there is still the need to determine the marginal distribution of the unobserved variable X, which is treated as a random variable. Provided appropriate functions for the mean and variance are stated, the regression parameters can be estimated consistently. We illust.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090413
Source:
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper58.ps.Z
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper58.ps.Z
Minimize
Document Type:
text
Language:
en
Subjects:
measurement error ; quasi likelihood ; Poisson regression ; radon data
measurement error ; quasi likelihood ; Poisson regression ; radon data
Minimize
DDC:
310 Collections of general statistics
(computed)
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
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6926
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6926
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
4.
Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
Open Access
Title:
Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
Author:
Markus Thamerus
Markus Thamerus
Minimize authors
Description:
We consider the case where a latent variable X cannot be observed directly and instead a variable W = X +U with an heteroscedastic measurement error U is observed. It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to find approximate ML estimates of the distribution param...
We consider the case where a latent variable X cannot be observed directly and instead a variable W = X +U with an heteroscedastic measurement error U is observed. It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to find approximate ML estimates of the distribution parameters of X . Keywords: Measurement error; EM algorithm; Finite mixture distribution 1 Introduction It is well known that measurement errors in the covariates of a regression model lead to biased parameter estimates. Most likelihood based methods that adjust for this effect treat the true predictor X as a stochastic variable and require an assumption about the marginal distribution of X, see e.g. Carroll, Ruppert and Stefanski (1995). Usually an unimodal distribution is assumed and without external knowledge its parameters have to be estimated from the observed data. But if the observations suggest that the underlying statistical population of .
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090413
Source:
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper48.ps.Z
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper48.ps.Z
Minimize
Document Type:
text
Language:
en
Subjects:
Measurement error ; EM algorithm ; Finite mixture distribution
Measurement error ; EM algorithm ; Finite mixture distribution
Minimize
DDC:
310 Collections of general statistics
(computed)
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
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.7668
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.7668
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
5.
Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
Open Access
Title:
Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
Author:
Markus Thamerus
Markus Thamerus
Minimize authors
Description:
We consider the case where a latent variable X cannot be observed directly and instead a variable W = X + U with an heteroscedastic measurement error U is observed. It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to nd approximate ML estimates of the distribution parame...
We consider the case where a latent variable X cannot be observed directly and instead a variable W = X + U with an heteroscedastic measurement error U is observed. It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to nd approximate ML estimates of the distribution parameters of X.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090203
Source:
http://epub.ub.unimuenchen.de/1445/1/paper_48.pdf
http://epub.ub.unimuenchen.de/1445/1/paper_48.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Measurement error � EM algorithm � Finite mixture distribution
Measurement error � EM algorithm � Finite mixture distribution
Minimize
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
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.5355
http://epub.ub.unimuenchen.de/1445/1/paper_48.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.5355
http://epub.ub.unimuenchen.de/1445/1/paper_48.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
6.
Different Nonlinear Regression Models with Incorrectly Observed Covariates
Open Access
Title:
Different Nonlinear Regression Models with Incorrectly Observed Covariates
Author:
Markus Thamerus
Markus Thamerus
Minimize authors
Description:
We present quasilikelihood models for different regression problems when one of the explanatory variables is measured with heteroscedastic error. In order to derive models for the observed data the conditional mean and variance functions of the regression models are only expressed through functions of the observable covariates. The latent covar...
We present quasilikelihood models for different regression problems when one of the explanatory variables is measured with heteroscedastic error. In order to derive models for the observed data the conditional mean and variance functions of the regression models are only expressed through functions of the observable covariates. The latent covariable is treated as a random variable that follows a normal distribution. Furthermore it is assumed that enough additional information is provided to estimate the individual measurement error variances, e.g. through replicated measurements of the fallible predictor variable. The discussion includes the polynomial regression model as well as the probit and logit model for binary data, the Poisson model for count data and ordinal regression models. Keywords: heteroscedastic measurement error, quasilikelihood, polynomial regression, Poisson model, binary regression models, ordinal regression models 1 Introduction It is a familiar situation for pr.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090413
Source:
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper68.ps.Z
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper68.ps.Z
Minimize
Document Type:
text
Language:
en
Subjects:
heteroscedastic measurement error ; quasilikelihood ; polynomial regression ; Poisson model
heteroscedastic measurement error ; quasilikelihood ; polynomial regression ; Poisson model
Minimize
DDC:
310 Collections of general statistics
(computed)
;
519 Probabilities & applied mathematics
(computed)
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
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6190
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6190
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
7.
Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
Title:
Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error
Author:
Thamerus, Markus
Thamerus, Markus
Minimize authors
Description:
We consider the case where a latent variable X cannot be observed directly and instead a variable W=X+U with an heteroscedastic measurement error U is observed. It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to find approximate ML estimates of the distribution paramete...
We consider the case where a latent variable X cannot be observed directly and instead a variable W=X+U with an heteroscedastic measurement error U is observed. It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to find approximate ML estimates of the distribution parameters of X.
Minimize
Year of Publication:
19960101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Minimize
Relations:
http://epub.ub.unimuenchen.de/1445/1/paper_48.pdf ; Thamerus, Markus (1996): Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error. Sonderforschungsbereich 386, Discussion Paper 48
URL:
http://epub.ub.unimuenchen.de/1445/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14457
http://epub.ub.unimuenchen.de/1445/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14457
Minimize
Content Provider:
LudwigMaximiliansUniversity Munich: Open Access LMU
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
8.
Modelling Count Data with Heteroscedastic Measurement Error in the Covariates
Title:
Modelling Count Data with Heteroscedastic Measurement Error in the Covariates
Author:
Thamerus, Markus
Thamerus, Markus
Minimize authors
Description:
This paper is concerned with the estimation of the regression coefficients for a count data model when one of the explanatory variables is subject to heteroscedastic measurement error. The observed values W are related to the true regressor X by the additive error model W=X+U. The errors U are assumed to be normally distributed with zero mean bu...
This paper is concerned with the estimation of the regression coefficients for a count data model when one of the explanatory variables is subject to heteroscedastic measurement error. The observed values W are related to the true regressor X by the additive error model W=X+U. The errors U are assumed to be normally distributed with zero mean but heteroscedastic variances, which are known or can be estimated from repeated measurements. Inference is done by using quasi likelihood methods, where a model of the observed data is specified only through a mean and a variance function for the response Y given W and other correctly observed covariates. Although this approach weakens the assumption of a parametric regression model, there is still the need to determine the marginal distribution of the unobserved variable X, which is treated as a random variable. Provided appropriate functions for the mean and variance are stated, the regression parameters can be estimated consistently. We illustrate our methods through an analysis of lung cancer rates in Switzerland. One of the covariates, the regional radon averages, cannot be measured exactly due to the strong dependency of radon on geological conditions and various other environmental sources of influence. The distribution of the unobserved true radon measure is modelled as a finite mixture of normals.
Minimize
Year of Publication:
19970101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Minimize
DDC:
310 Collections of general statistics
(computed)
Relations:
http://epub.ub.unimuenchen.de/1452/1/paper_58.pdf ; Thamerus, Markus (1997): Modelling Count Data with Heteroscedastic Measurement Error in the Covariates. Sonderforschungsbereich 386, Discussion Paper 58
URL:
http://epub.ub.unimuenchen.de/1452/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14526
http://epub.ub.unimuenchen.de/1452/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14526
Minimize
Content Provider:
LudwigMaximiliansUniversity Munich: Open Access LMU
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
9.
Different Nonlinear Regression Models with Incorrectly Observed Covariates
Title:
Different Nonlinear Regression Models with Incorrectly Observed Covariates
Author:
Thamerus, Markus
Thamerus, Markus
Minimize authors
Description:
We present quasilikelihood models for different regression problems when one of the explanatory variables is measured with heteroscedastic error. In order to derive models for the observed data the conditional mean and variance functions of the regression models are only expressed through functions of the observable covariates. The latent covar...
We present quasilikelihood models for different regression problems when one of the explanatory variables is measured with heteroscedastic error. In order to derive models for the observed data the conditional mean and variance functions of the regression models are only expressed through functions of the observable covariates. The latent covariable is treated as a random variable that follows a normal distribution. Furthermore it is assumed that enough additional information is provided to estimate the individual measurement error variances, e.g. through replicated measurements of the fallible predictor variable. The discussion includes the polynomial regression model as well as the probit and logit model for binary data, the Poisson model for count data and ordinal regression models.
Minimize
Year of Publication:
19970101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Minimize
Relations:
http://epub.ub.unimuenchen.de/1462/1/paper_68.pdf ; Thamerus, Markus (1997): Different Nonlinear Regression Models with Incorrectly Observed Covariates. Sonderforschungsbereich 386, Discussion Paper 68
URL:
http://epub.ub.unimuenchen.de/1462/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14621
http://epub.ub.unimuenchen.de/1462/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14621
Minimize
Content Provider:
LudwigMaximiliansUniversity Munich: Open Access LMU
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
10.
Extreme value analysis of Munich airpollution data
Title:
Extreme value analysis of Munich airpollution data
Author:
Küchenhoff, Helmut
;
Thamerus, Markus
Küchenhoff, Helmut
;
Thamerus, Markus
Minimize authors
Description:
We present three different approaches to model extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data the parameters of the generalized Pareto distribution were estimated. Accounting for aut...
We present three different approaches to model extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation clusters of exceedances were used. To get information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of ozone and of nitrogendioxid at two monitoring sites within the city of Munich.
Minimize
Year of Publication:
19950101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Minimize
Relations:
http://epub.ub.unimuenchen.de/1408/1/paper_04.pdf ; Küchenhoff, Helmut und Thamerus, Markus (1995): Extreme value analysis of Munich airpollution data. Sonderforschungsbereich 386, Discussion Paper 4
URL:
http://epub.ub.unimuenchen.de/1408/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14082
http://epub.ub.unimuenchen.de/1408/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14082
Minimize
Content Provider:
LudwigMaximiliansUniversity Munich: Open Access LMU
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
Export Record
All Records
Export
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Adjust your hit list
Sort Your Results
Refine Search Result
More Options
Sort Your Results
Sort by:
Relevance
Author, ZA
Author, AZ
Title, AZ
Title, ZA
Date of publication, descending
Date of publication, ascending
Refine Search Result
Author
(6) Markus Thamerus
(5) Thamerus, Markus
(5) The Pennsylvania State University CiteSeerX...
(1) Cheng, ChiLun
(1) ChiLun Cheng
(1) Hans Schneeweiss
(1) Helmut Küchenhoff
(1) Küchenhoff, Helmut
(1) Schneeweiß, Hans
Author:
Subject
(5) ddc 510
(5) sonderforschungsbereich 386
(2) measurement error
(2) quasi likelihood
(1) air pollution
(1) em algorithm
(1) extreme values
(1) finite mixture distribution
(1) generalized extreme value distribution
(1) generalized pareto distribution
(1) heteroscedastic measurement error
(1) logistic regression
(1) measurement error em algorithm finite mixture...
(1) poisson model
(1) poisson regression
(1) polynomial regression
(1) radon data
Subject:
Dewey Decimal Classification (DDC)
(5) Statistics [31*]
(1) Economics [33*]
(1) Mathematics [51*]
Dewey Decimal Classification (DDC):
Year of Publication
(5) 2009
(2) 1997
(1) 1995
(1) 1996
(1) 1998
Year of Publication:
Content Provider
(5) CiteSeerX
(5) Munich LMU: Open Access
(1) RePEc.org
Content Provider:
Language
(6) Unknown
(5) English
Language:
Document Type
(5) Text
(5) Reports, Papers, Lectures
(1) Article, Journals
Document Type:
Access
(6) Unknown
(5) Open Access
Access:
More Options
»
Search History
»
Get RSS Feed
»
Get ATOM Feed
»
Email this Search
»
Save Search
»
Browsing
»
Search Plugin
Further result pages
Results:
1

2
Next »
New Search »
Currently in BASE: 72,045,933 Documents of 3,464
Content Sources
About BASE

Contact

BASE Lab

Imprint
© 20042015 by
Bielefeld University Library
Search powered by
Solr
&
VuFind
.
Suggest Repository
BASE Interfaces
Currently in BASE: 72,045,933 Documents of 3,464 Content Sources
http://www.basesearch.net