null
Loading... Please wait...
FREE SHIPPING on All Unbranded Items LEARN MORE
Print This Page

Predictive Inference

List Price: $87.99
SKU:
9780367449919
Quantity:
Minimum Purchase
25 unit(s)
  • Availability: Confirm prior to ordering
  • Branding: minimum 50 pieces (add’l costs below)
  • Check Freight Rates (branded products only)

Branding Options (v), Availability & Lead Times

  • 1-Color Imprint: $2.00 ea.
  • Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
  • Belly-Band Wrap: $2.50 ea. (full-color printed)
  • Set-Up Charge: $45 per decoration
FULL DETAILS
  • Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
  • Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
  • Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
  • RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
  • Product Details

    Author:
    Seymour Geisser
    Format:
    Paperback
    Pages:
    276
    Publisher:
    CRC Press (December 3, 2019)
    Language:
    English
    ISBN-13:
    9780367449919
    Weight:
    11.875oz
    Dimensions:
    5.4375" x 8.5"
    File:
    TAYLORFRANCIS-TayFran_260409051851155-20260409.xml
    Folder:
    TAYLORFRANCIS
    List Price:
    $87.99
    Series:
    Chapman & Hall/CRC Monographs on Statistics and Applied Probability
    Case Pack:
    20
    As low as:
    $83.59
    Publisher Identifier:
    P-CRC
    Discount Code:
    H
    Audience:
    Professional and scholarly
    Country of Origin:
    United States
    Pub Discount:
    30
    Imprint:
    Chapman and Hall/CRC
  • Overview

    The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.