skip to content
Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 Preview this item
ClosePreview this item
Checking...

Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983

Author: Jürgen Franke; Wolfgang Hardle; Douglas Martin
Publisher: New York, NY : Springer US, 1984.
Series: Lecture notes in statistics (Springer-Verlag), 26.
Edition/Format:   eBook : Bibliographic data : EnglishView all editions and formats
Summary:
Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

 

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Bibliographic data, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jürgen Franke; Wolfgang Hardle; Douglas Martin
ISBN: 9781461578215 1461578213 9780387961026 038796102X
OCLC Number: 840286427
Description: 1 online resource (286 pages).
Contents: On the Use of Bayesian Models in Time Series Analysis --
Order Determination for Processes with Infinite Variance --
Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models --
Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks --
Linear Error-in-Variables Models --
Minimax-Robust Filtering and Finite-Length Robust Predictors --
The Problem of Unsuspected Serial Correlations --
The Estimation of ARMA Processes --
How to Determine the Bandwidth of some Nonlinear Smoothers in Practice --
Remarks on NonGaussian Linear Processes with Additive Gaussian Noise --
Gross-Error Sensitivies of GM and RA-Estimates --
Some Aspects of Qualitative Robustness in Time Series --
Tightness of the Sequence of Empiric C.D.F. Processes Defined from Regression Fractiles --
Robust Nonparametric Autoregression --
Robust Regression by Means of S-Estimators --
On Robust Estimation of Parameters for Autoregressive Moving Average Models.
Series Title: Lecture notes in statistics (Springer-Verlag), 26.
Responsibility: edited by J.rgen Franke, Wolfgang H.rdle, Douglas Martin.

Abstract:

Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.

Similar Items

Related Subjects:(1)

Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/840286427> # Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
    a schema:Book, schema:CreativeWork ;
   library:oclcnum "840286427" ;
   library:placeOfPublication <http://id.loc.gov/vocabulary/countries/nyu> ;
   library:placeOfPublication <http://experiment.worldcat.org/entity/work/data/180827943#Place/new_york_ny> ; # New York, NY
   rdfs:comment "Unknown 'gen' value: bdt" ;
   schema:about <http://dewey.info/class/519.5/e23/> ;
   schema:about <http://id.worldcat.org/fast/1132103> ; # Statistics
   schema:bookFormat schema:EBook ;
   schema:contributor <http://viaf.org/viaf/274017771> ; # Douglas Martin
   schema:contributor <http://viaf.org/viaf/39441475> ; # Wolfgang Hardle
   schema:creator <http://viaf.org/viaf/122139792> ; # Jürgen Franke
   schema:datePublished "1984" ;
   schema:description "On the Use of Bayesian Models in Time Series Analysis -- Order Determination for Processes with Infinite Variance -- Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models -- Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks -- Linear Error-in-Variables Models -- Minimax-Robust Filtering and Finite-Length Robust Predictors -- The Problem of Unsuspected Serial Correlations -- The Estimation of ARMA Processes -- How to Determine the Bandwidth of some Nonlinear Smoothers in Practice -- Remarks on NonGaussian Linear Processes with Additive Gaussian Noise -- Gross-Error Sensitivies of GM and RA-Estimates -- Some Aspects of Qualitative Robustness in Time Series -- Tightness of the Sequence of Empiric C.D.F. Processes Defined from Regression Fractiles -- Robust Nonparametric Autoregression -- Robust Regression by Means of S-Estimators -- On Robust Estimation of Parameters for Autoregressive Moving Average Models."@en ;
   schema:description "Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model."@en ;
   schema:exampleOfWork <http://worldcat.org/entity/work/id/180827943> ;
   schema:genre "Electronic books"@en ;
   schema:inLanguage "en" ;
   schema:isPartOf <http://experiment.worldcat.org/entity/work/data/180827943#Series/lecture_notes_in_statistics_springer_verlag> ; # Lecture notes in statistics (Springer-Verlag) ;
   schema:isPartOf <http://experiment.worldcat.org/entity/work/data/180827943#Series/lecture_notes_in_statistics_0930_0325> ; # Lecture Notes in Statistics, 0930-0325 ;
   schema:isSimilarTo <http://worldcat.org/entity/work/data/180827943#CreativeWork/> ;
   schema:name "Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983"@en ;
   schema:productID "840286427" ;
   schema:publication <http://www.worldcat.org/title/-/oclc/840286427#PublicationEvent/new_york_ny_springer_us_1984> ;
   schema:publisher <http://experiment.worldcat.org/entity/work/data/180827943#Agent/springer_us> ; # Springer US
   schema:url <http://link.springer.com/10.1007/978-1-4615-7821-5> ;
   schema:url <http://public.eblib.com/choice/publicfullrecord.aspx?p=3083466> ;
   schema:url <http://dx.doi.org/10.1007/978-1-4615-7821-5> ;
   schema:workExample <http://dx.doi.org/10.1007/978-1-4615-7821-5> ;
   schema:workExample <http://worldcat.org/isbn/9781461578215> ;
   schema:workExample <http://worldcat.org/isbn/9780387961026> ;
   wdrs:describedby <http://www.worldcat.org/title/-/oclc/840286427> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/180827943#Series/lecture_notes_in_statistics_0930_0325> # Lecture Notes in Statistics, 0930-0325 ;
    a bgn:PublicationSeries ;
   schema:hasPart <http://www.worldcat.org/oclc/840286427> ; # Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
   schema:name "Lecture Notes in Statistics, 0930-0325 ;" ;
    .

<http://experiment.worldcat.org/entity/work/data/180827943#Series/lecture_notes_in_statistics_springer_verlag> # Lecture notes in statistics (Springer-Verlag) ;
    a bgn:PublicationSeries ;
   schema:hasPart <http://www.worldcat.org/oclc/840286427> ; # Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
   schema:name "Lecture notes in statistics (Springer-Verlag) ;" ;
    .

<http://id.worldcat.org/fast/1132103> # Statistics
    a schema:Intangible ;
   schema:name "Statistics"@en ;
    .

<http://link.springer.com/10.1007/978-1-4615-7821-5>
   rdfs:comment "from Springer" ;
   rdfs:comment "(Unlimited Concurrent Users)" ;
    .

<http://viaf.org/viaf/122139792> # Jürgen Franke
    a schema:Person ;
   schema:familyName "Franke" ;
   schema:givenName "Jürgen" ;
   schema:name "Jürgen Franke" ;
    .

<http://viaf.org/viaf/274017771> # Douglas Martin
    a schema:Person ;
   schema:familyName "Martin" ;
   schema:givenName "Douglas" ;
   schema:name "Douglas Martin" ;
    .

<http://viaf.org/viaf/39441475> # Wolfgang Hardle
    a schema:Person ;
   schema:familyName "Hardle" ;
   schema:givenName "Wolfgang" ;
   schema:name "Wolfgang Hardle" ;
    .

<http://worldcat.org/entity/work/data/180827943#CreativeWork/>
    a schema:CreativeWork ;
   schema:description "Printed edition:" ;
   schema:isSimilarTo <http://www.worldcat.org/oclc/840286427> ; # Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
    .

<http://worldcat.org/isbn/9780387961026>
    a schema:ProductModel ;
   schema:isbn "038796102X" ;
   schema:isbn "9780387961026" ;
    .

<http://worldcat.org/isbn/9781461578215>
    a schema:ProductModel ;
   schema:isbn "1461578213" ;
   schema:isbn "9781461578215" ;
    .

<http://www.worldcat.org/title/-/oclc/840286427>
    a genont:InformationResource, genont:ContentTypeGenericResource ;
   schema:about <http://www.worldcat.org/oclc/840286427> ; # Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
   schema:dateModified "2017-12-23" ;
   void:inDataset <http://purl.oclc.org/dataset/WorldCat> ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.