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Articles 1 - 8 of 8
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 …
Local Spectral Analysis Via A Bayesian Mixture Of Smoothing Splines” Journal Of The American Statistical Association, Sally Wood, Ori Rosen, David Stoffer
Local Spectral Analysis Via A Bayesian Mixture Of Smoothing Splines” Journal Of The American Statistical Association, Sally Wood, Ori Rosen, David Stoffer
Sally Wood
No abstract provided.
A Bayesian Approach To Ordinal Outcomes For Neurosurgical Clinical Research., Sally Wood
A Bayesian Approach To Ordinal Outcomes For Neurosurgical Clinical Research., Sally Wood
Sally Wood
The objective of this study is to demonstrate a Bayesian approach for the statistical analysis of neurosurgical data where the investigators have used an ordinal scale for the outcome. A Bayesian approach that uses data augmentation and Gibbs sampling to perform ordinal probit regression is demonstrated in a neurosurgical context. The statistical approach is applied to a regression analysis to examine the relationship between female gender and the outcome of severe traumatic brain injury measured with the Glascow Outcome Scale. The approach is applied to a hierarchical meta-analysis to examine the relationship between age and the outcome from subarachnoid haemorrhage …
Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie
Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie
Sally Wood
A general Bayesian sampling method is developed that uses parallel chains to select between models and to average the predictive density over such models. The method applies to both non-nested models and to nested models, and is particularly useful for mixtures of complex component models, where a novel approach to overcome the label-switching problem is used. The method is illustrated with real and simulated data in model-averaging over alternative financial time series models, mixtures of normal distributions, and mixtures of smoothing spline models.
Mixture Of Random Effects For Individual Learning Curves, Sally Wood, Edward Cripps, Robert Wood
Mixture Of Random Effects For Individual Learning Curves, Sally Wood, Edward Cripps, Robert Wood
Sally Wood
In the pyschology literature individuals are often classified as entity theorists or incrementalists. In this paper we explore the different learning behaviours over time of these two groups. To assess learning an individual is assigned a task and their performance on the task is measured over a number of trials. Learning behaviour is modelled as a mixture of two random effects, where the random effects components of the mixture correspond to increased learning and spiralling behaviour. We find significant differences in the learning behaviours of the two groups. Specifically those individuals who are categorized as entity theorists are more likely …
Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang
Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang
Sally Wood
This article proposes a new prior specification for a Bayesian analysis of the k largest order statistics model. We show that using Jeffreys priors for the end-point and shape parameters of the k largest order statistics model leads to biased estimates of the shape parameter for small to medium sample sizes and to the posterior mode of the end-point being equal to the most extreme observed value. We propose a conjugate prior for the shape parameter and a prior for the end-point which removes the posterior mode at the most extreme observed value while remaining uninformative for values of the …
Locally Adaptive Nonparametric Binary Regression, Sally Wood, Martin Tanner, Wenxin Jiang, Robert Kohn, Remy Cottet
Locally Adaptive Nonparametric Binary Regression, Sally Wood, Martin Tanner, Wenxin Jiang, Robert Kohn, Remy Cottet
Sally Wood
No abstract provided.