Accession Number:

ADA191157

Title:

Partial Likelihood Analysis of Time Series Models, with Application to Rainfall-Runoff Data,

Descriptive Note:

Corporate Author:

MARYLAND UNIV COLLEGE PARK DEPT OF MATHEMATICS

Personal Author(s):

• Slud, Eric V.
• Kedem, Benjamin

Report Date:

1988-02-25

Pagination or Media Count:

25.0

Abstract:

A general logistic-autoregressive model for binary time series or longitudinal responses is presented, generalizing the discrete-time Cox 1972 model with time-dependent covariates as well as the recent regression models of Kaufmann 1987 for categorical time-series. Since this model is formulated in terms of the time-series covariates which are not themselves explicitly modelled, the large-sample theory of parameter-estimation must be justified by means of Partial Likelihood in the sense of Cox 1975, using theoretical results like those of Wong 1986. The large-sample theory also justifies goodness of fit tests analogous to the chi-squared tests of Schoenfeld 1980 and to the tests based on sums of normalized squared residuals used in logistic regression. These ideas are illustrated by analysis of a rainfall-runoff hydrological dataset previously analyzed by Yakowitz 1987.

Descriptors:

• *MATHEMATICAL MODELS
• *TIME SERIES ANALYSIS
• *APPLIED MATHEMATICS
• *RAINFALL
• *RUNOFF
• LOGISTICS
• REGRESSION ANALYSIS
• HYDROLOGY
• MAXIMUM LIKELIHOOD ESTIMATION
• TIME DEPENDENCE

Subject Categories:

• Numerical Mathematics
• Meteorology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE