SFr. 74.00
€ 79.92
BTC 0.0013
LTC 0.979
ETH 0.0252


bestellen

Artikel-Nr. 5302331


Diesen Artikel in meine
Wunschliste
Diesen Artikel
weiterempfehlen
Diesen Preis
beobachten

Weitersagen:


Herausgeber: 
  • David Dunson
  • Random Effect and Latent Variable Model Selection 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 14-24 Tagen versandfertig
    Veröffentlichung:  August 2008  
    Genre:  Schulbücher 
     
    B / Mathematics and Statistics / Probabilities / Probability & statistics / Probability Theory / Probability Theory and Stochastic Processes / Statistical Theory and Methods / Statistics / Stochastics
    ISBN:  9780387767208 
    EAN-Code: 
    9780387767208 
    Verlag:  Springer Nature Singapore 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Lecture Notes in Statistics  
    Dimensionen:  H 235 mm / B 154 mm / D 13 mm 
    Gewicht:  272 gr 
    Seiten:  170 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Random effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predictive models. However, classical methods for model comparison are not well justified in such settings.

    This book presents state of the art methods for accommodating model uncertainty in random effects and latent variable models. It will appeal to students, applied data analysts, and experienced researchers. The chapters are based on the contributors' research, with mathematical details minimized using applications-motivated descriptions.

    The first part of the book focuses on frequentist likelihood ratio and score tests for zero variance components. Contributors include Xihong Lin, Daowen Zhang and Ciprian Crainiceanu.

    The second part focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models. Contributors include David Dunson and collaborators Bo Cai and Saki Kinney.

    The final part focuses on structural equation models, with Peter Bentler and Jiajuan Liang presenting a frequentist approach, Sik-Yum Lee and Xin-Yuan Song presenting a Bayesian approach based on path sampling, and Joyee Ghosh and David Dunson proposing a method for default prior specification and efficient posterior computation.

    David Dunson is Professor in the Department of Statistical Science at Duke University. He is an international authority on Bayesian methods for correlated data, a fellow of the American Statistical Association, and winner of the David Byar and Mortimer Spiegelman Awards.

      



    Wird aktuell angeschaut...
     

    Zurück zur letzten Ansicht


    AGB | Datenschutzerklärung | Mein Konto | Impressum | Partnerprogramm
    Newsletter | 1Advd.ch RSS News-Feed Newsfeed | 1Advd.ch Facebook-Page Facebook | 1Advd.ch Twitter-Page Twitter
    Forbidden Planet AG © 1999-2024
    Alle Angaben ohne Gewähr
     
    SUCHEN

     
     Kategorien
    Im Sortiment stöbern
    Genres
    Hörbücher
    Aktionen
     Infos
    Mein Konto
    Warenkorb
    Meine Wunschliste
     Kundenservice
    Recherchedienst
    Fragen / AGB / Kontakt
    Partnerprogramm
    Impressum
    © by Forbidden Planet AG 1999-2024