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

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).

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Document Details

Document Type
Technical Report
Publication Date
Feb 25, 1988
Accession Number
ADA191157

Entities

People

  • Benjamin Kedem
  • Eric V. Slud

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Coefficients
  • Data Analysis
  • Data Mining
  • Data Science
  • Efficiency
  • Goodness Of Fit Tests
  • Information Science
  • Maryland
  • Mathematics
  • Military Research
  • Probabilistic Models
  • Probability
  • Random Variables
  • Residuals
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Coastal and Marine Engineering/Sediment Transport/Hydraulic Engineering
  • Neural Network Machine Learning.
  • Statistical inference.