Prediction and classification of non-stationary categorical time series
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Publication:1275416
DOI10.1006/jmva.1998.1765zbMath0919.62105OpenAlexW2063890903MaRDI QIDQ1275416
Konstantinos Fokianos, Benjamin Kedem-Kimelfeld
Publication date: 16 August 1999
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/1903/5768
Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Generalized linear models (logistic models) (62J12)
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Uses Software
Cites Work
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- Cox's regression model for counting processes: A large sample study
- Theory of partial likelihood
- On existence and uniqueness of maximum likelihood estimates in quantal and ordinal response models
- Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
- Regression models for nonstationary categorical time series: Asymptotic estimation theory
- Logistic Regression for Dependent Binary Observations
- A Class of Logistic Regression Models for Multivariate Binary Time Series
- Chi-squared goodness-of-fit tests for the proportional hazards regression model
- Concavity of the Log Likelihood
- Partial likelihood
- On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models
- REGRESSION MODELS FOR NON‐STATIONARY CATEGORICAL TIME SERIES
- A Time Series Analysis of Binary Data
- Time Series Models Based on Generalized Linear Models: Some Further Results
- Projected partial likelihood and its application to longitudinal data
- The Lindeberg-Levy Theorem for Martingales
- Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems
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