Bayesian Model Averaging and Semiparametric Regression

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Bayesian Identification, Selection and Estimation of Functions in High-Dimensional Additive Models (with Anastasios Panagiotelis), Journal of Econometrics (2008)
In this paper we propose an approach to both estimate and select unknown smooth functions...
 

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'Parsimonious Covariance Matrix Estimation for Longitudinal Data (with Robert Kohn), Journal of the American Statistical Association (2002)
This article proposes a data-driven method to identify parsimony in the covariance matrix of longitudinal...
 

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Nonparametric regression using linear combinations of basis functions (with Robert Kohn and D Chan), Statistics and Computing (2001)
This paper discusses a Bayesian approach to nonparametric regression initially proposed by Smith and Kohn...
 

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Nonparametric seemingly unrelated regression (with Robert Kohn), Journal of Econometrics (2000)
A method is presented for simultaneously estimating a system of nonparametric regressions which may seem...
 

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Semiparametric Regression: An Exposition and Application to Print Advertising Data (with Robert Kohn and Sharat K. Mathur), Journal of Business Research (2000)
A new regression based approach is proposed for modeling marketing databases. The approach is Bayesian...
 

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Additive nonparametric regression with autocorrelated errors (with C Wong and Robert Kohn), Journal of the Royal Statistical Society, Series B (1998)
 

Bayesian Spatial Smoothing

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Spatial Bayesian variable selection with application to functional magnetic resonance imaging (with Ludwig Fahrmeir), Journal of the American Statistical Association (2007)
In this paper we propose a procedure to undertake Bayesian variable selection and model averaging...
 

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Estimation of Binary Markov Random Fields using Markov Chain Monte Carlo (with Daniel Smith), Journal of Computational and Graphical Statistics (2006)
This article compares three binary Markov random fields (MRFs) which are popular Bayesian priors for...
 

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Assessing brain activity using spatial Bayesian variable selection (with B Putz, D Auer, and Ludwig Fahrmeir), NeuroImage (2003)
Statistical parametric mapping (SPM), relying on the general linear model and classical hypothesis testing, is...
 

Forecasting and Time Series

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Bayesian Density Forecasting of Intraday Electricity Prices using Multivariate Skew t Distributions (with Anastasios Panagiotelis), International Journal of Forecasting (2008)
Electricity spot prices exhibit strong time series properties, including substantial periodicity, both inter-day and intraday...
 

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Foreign exchange intervention by the Bank of Japan: Bayesian analysis using a bivariate stochastic volatility model (with Andrew Pitts), Econometric Reviews (2006)
A bivariate stochastic volatility model is employed to measure the effect of intervention by the...
 

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Bayesian modelling and forecasting of intra-day electricity load (with Remy Cottet), Journal of the American Statistical Association (2003)
With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity...
 

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Estimating long term trends in tropospheric ozone (with Paul Yau, Tom Shively, and Robert Kohn), International Statistical Review (2002)
This paper develops methodology for estimating long ­term trends in the daily maxima of tropospheric...
 

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Short-term forecasting of New South Wales electricity system load, Journal of Business and Economic Statistics (2000)
 

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Additive nonparametric regression with autocorrelated errors (with C Wong and Robert Kohn), Journal of the Royal Statistical Society, Series B (1998)
 

Multivariate Models in Marketing

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Modeling Multivariate Distributions Using Copulas: Applications in Marketing (with Peter J. Danaher), Forthcoming in Marketing Science (2009)
In this research we introduce a new class of multivariate probability models to the marketing...
 

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Semiparametric Regression: An Exposition and Application to Print Advertising Data (with Robert Kohn and Sharat K. Mathur), Journal of Business Research (2000)
A new regression based approach is proposed for modeling marketing databases. The approach is Bayesian...