Accession Number : ADA191157

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

Corporate Author : MARYLAND UNIV COLLEGE PARK DEPT OF MATHEMATICS

Personal Author(s) : Slud, Eric V ; Kedem, Benjamin

PDF Url : ADA191157

Report Date : 25 Feb 1988

Pagination or Media Count : 25

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