Open Access. Powered by Scholars. Published by Universities.®
- Keyword
Articles 1 - 2 of 2
Full-Text Articles in Business
Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn
Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn
Sally Wood
In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. The model parameters, including the number of mixture components, are estimated via Markov chain Monte Carlo methods. The methodology is illustrated with simulated and real data.
Modelling The Impact Of Personality On Individual Performance Behavior With A Time-Varying Mixture Of Monotonic Random Effects, Sally A. Wood, Edward J. Cripps, Robert E. Wood, John Lau
Modelling The Impact Of Personality On Individual Performance Behavior With A Time-Varying Mixture Of Monotonic Random Effects, Sally A. Wood, Edward J. Cripps, Robert E. Wood, John Lau
Sally Wood
A method is presented for flexibly modelling longitudinal data that provides insight to a central question in psychology theory: the dependency between personality clas- sification and individual performance behavior. Flexibility is achieved by assuming the regression coefficients of random effects models are generated from a time-varying mixture of an unknown but finite number of processes, where the weights attached to the number of processes are parameterised to depend upon an individual’s personality classification. For a given number of mixture components the component processes are constrained distributions and the weights attached to them depend upon time. The method is made robust …