Ameen, Jamal Rasul Mohammad (1984) Discount Bayesian models and forecasting. PhD thesis, University of Warwick.

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Abstract

This thesis is concerned with Bayesian forecasting and sequential estimation. The
concept of multiple discounting is introduced in order to achieve parametric and
conceptual parsimony. In addition, this overcomes many of the drawbacks of the Normal
Dynamic Linear Model (DLM) specification which uses a system variance matrix. These
drawbacks involve ambiguity and invariance to the scale of independent variables. A
class of Normal Discount Bayesian Models (NDBM) is introduced to overcome these
difficulties. Facilities for parameter learning and multiprocess modelling are provided.
Unlike the DLM's, many limiting results are easily obtained for NDBMM's. A general class
of Normal Weighted Bayesian Models (NWBM) is introduced. This includes the class of
DLM's as a special case. Other important subclasses of Extended and Modified NWBM's
are also introduced. These are particularly useful in modelling discontinuities and for
systems which operates according to the principle of Management by Exception. A
number of illustrative applications are given.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Time-series analysis, Sequences (Mathematics)
Official Date:	May 1984
Dates:	Date Event May 1984 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Harrison, P. J.
Sponsors:	Jāmiʻat al-Sulaymānīyah [University of Sulaimani] ; Iraq. Wizārat al-Taʻlīm al-ʻAlī wa-al-Baḥth al-ʻIlmī [Ministry of Higher Education and Scientific Research]
Extent:	viii, 108 p.
Language:	eng

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