Reply to the Discussion of Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Resource

Abstract

The paper s4Space-time modelling with long-memory dependence: Assessing Ireland's wind power resource (Technical Report No. 110,Department of Statistics, University of Washington) was read before the Royal Statistical Society as a meeting organized by the Research Section on May 25,1988. There were 33 discussants, who between them made more than 100 separate suggestions and queries. This is the reply to the Discussion; the contributions to the discussion are included as an Appendix. Many of the discussants were concerned that model used was not sufficiently general. We argue that this is not a problem for the present application, but we do propose a more general model for use in other contexts. This allows for non-homogeneity of temporal dependence across sites and for anisotropic spatial correlation. We review the evidence for long-memory dependence as opposed to non-stationarity, and for the use of fractional differencing to model it. We discuss computational and asymptotic aspects of the estimation of the fractional differencing parameter, the location parameter of the wind speed distribution, and the distribution of wind power. Many other points are discussed, including the order in which transformation and aggregation are carried out and the treatment of the outlier rosslare.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1988
Accession Number
ADA201678

Entities

People

  • Adrian Raftery
  • John Haslett

Organizations

  • University of Washington

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Cross Correlation
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Ecology
  • Equations
  • Estimators
  • Information Science
  • Solar Energy
  • Square Roots
  • Statistical Algorithms
  • Statistical Analysis
  • Two Dimensional
  • Wave Power
  • Wind Energy
  • Wind Turbines

Fields of Study

  • Mathematics

Readers

  • Academic Conference Management
  • Business Analytics
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space