The Art of Data Augmentation

Dyk, David A. van; Meng, Xiao‐Li

doi:10.1198/10618600152418584

Cited by 862 publications

(493 citation statements)

References 49 publications

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“…Also, the return on an arbitrage portfolio based on portfolios sorted per degree of corporate governance may explain the cross-section of returns of individual stocks. We find supporting evidence for both claims in this research by multiply imputing missing values (Dempster et al, 1977;van Dyk and Meng, 2001). The remainder of this paper is organized as follows.…”

Section: Introduction supporting

confidence: 61%

How does corporate governace pay off? Evidence from Korean stock listings

Choi

Choi²,

Son

2016

Investment Management and Financial Innovations

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Corporate governance is an envelope for the mechanisms, processes and relations through which corporations are controlled and guided. Consequently, corporate governance affects operational performance and, in turn, stock returns, as Gompers et al. (2003) find. In this research, we use the Korea Corporate Governance Stock Price Index (KOGI) to test a possible linkage between corporate governance and shareholder wealth in Korea. Factor mimicking portfolios sorted per KOGI are constructed to estimate a corporate governance risk factor ("good minus bad"). By augmenting this new factor to the existing factor models (Fama and French, 1993;Carhart, 1997) to fit multiply imputed data, we find evidence that corporate governanceinfluences stock pricing in Korea.JEL Classification: G11, G12, G34, C11.

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Section: Introduction supporting

confidence: 61%

How does corporate governace pay off? Evidence from Korean stock listings

Choi

Choi²,

Son

2016

Investment Management and Financial Innovations

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“…Similar problems occur for the EM algorithm and (Liu, Rubin, and Wu 1998) introduced parameter expansion to speed up the rate of convergence. The idea was quickly applied to Gibbs sampling problems Liu and Wu (1999) and has now been extensively used to develop more efficient mixed-model samplers (e.g., van Dyk and Meng 2001;Gelman, van Dyk, Huang, and Boscardin 2008;Browne, Steele, Golalizadeh, and Green 2009).…”

Section: A5 Parameter Expansionmentioning

confidence: 99%

Variables associated with detection probability, detection latency, and behavioral responses of Golden-winged Warblers (Vermivora chrysoptera)

Aldinger

Wood

2015

The Condor

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Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm, implements such an algorithm for a range of model fitting problems. More than one response variable can be analysed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in meta-analysis. All simulation is done in C/ C++ using the CSparse library for sparse linear systems. If you use the software please cite this article, as published in the Journal of Statistic Software (Hadfield 2010) Keywords: MCMC, linear mixed model, pedigree, phylogeny, animal model, multivariate, sparse, R.Due to their flexibility, linear mixed models are now widely used across the sciences (Brown and Prescott 1999;Pinheiro and Bates 2000;Demidenko 2004). However, generalizing these models to non-Gaussian data has proved difficult because integrating over the random effects is intractable (McCulloch and Searle 2001). Although techniques that approximate these integrals (Breslow and Clayton 1993) are now popular, Markov chain Monte Carlo (MCMC) methods provide an alternative strategy for marginalizing the random effects that may be more robust (Zhao, Staudenmayer, Coull, and Wand 2006;Browne and Draper 2006). Developing MCMC methods for generalized linear mixed models (GLMM) is an active area of research (e.g., Zeger and Karim 1991;Damien, Wakefield, and Walker 1999;Sorensen and Gianola 2002;Zhao et al. 2006), and several software packages are now available that implement these techniques (e.g., WinBUGS (Spiegelhalter, Thomas, Best, and Lunn 2003), MLwiN (Rasbash, Steele, Browne, and Prosser 2005), glmmBUGS (Brown 2009), JAGS (Plummer 2003)). However, these methods often require a certain level of expertise on behalf of the user and may take a great deal of computing time. The MCMCglmm package for R (R Development Core Team 2009) implements Markov chain Monte Carlo routines for fitting multi-response generalized linear mixed models. A range of distributions are supported and several types of variance structure for the random effects and the residuals can be fitted. The aim is to provide routines that require little expertise on behalf of the user while reducing the 2 MCMCglmm amount of computing time required to adequately sample the posterior distribution.In this paper we explain the underlying ...

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“…For general details on PXDA readers are referred to Liu and Wu (1999) and van Dyk and Meng (2001). As conjugate priors do not exist on correlation matrices, PXDA allows an augmentation converting the correlation matrix R into a valid covariance matrix.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Bayesian Model Fusion for Forecasting Civil Unrest

et al. 2015

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With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and spurring them into action. We are interested in using Big Data approaches to generate forecasts of civil unrest from open source indicators. The heterogeneous nature of data coupled with the rich and diverse origins of civil unrest call for a multi-model approach to such forecasting. We present a modular approach wherein a collection of models use overlapping sources of data to independently forecast protests. Fusion of alerts into one single alert stream becomes a key system informatics problem and we present a statistical framework to accomplish such fusion. Given an alert from one of the numerous models, the decision space for fusion has two possibilities: i) release the alert or ii) suppress the alert. Using a Bayesian decision theoretic framework, we present a fusion approach for releasing or suppressing alerts. The resulting system enables real-time decisions and more importantly tuning of precision and recall.

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The Art of Data Augmentation

Cited by 862 publications

References 49 publications

How does corporate governace pay off? Evidence from Korean stock listings

How does corporate governace pay off? Evidence from Korean stock listings

Variables associated with detection probability, detection latency, and behavioral responses of Golden-winged Warblers (Vermivora chrysoptera)

Bayesian Model Fusion for Forecasting Civil Unrest

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