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Autor(en): 
  • Schweder Tore
  • Hjort Nils Lid
  • Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Februar 2016  
    Genre:  Schulbücher 
     
    Econometrics / Econometrics and economic statistics / MATHEMATICS / Probability & Statistics / General / Probability & statistics / Probability and statistics
    ISBN:  9780521861601 
    EAN-Code: 
    9780521861601 
    Verlag:  Cambridge Academic 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Cambridge Series in Statistical and Probabilistic Mathematics  
    Dimensionen:  H 260 mm / B 184 mm / D 31 mm 
    Gewicht:  1090 gr 
    Illustration:  147 b/w illus. 17 tables 100 exercises, Zeichnungen, nicht spezifiziert, Tabellen, nicht spezifiziert, Worked examples or Exercises 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. Neyman-Pearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources.

      



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