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57
Making the most of statistical analyses: Improving interpretation and presentation
 American Journal of Political Science
, 2000
"... Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. ..."
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Cited by 169 (18 self)
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Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a readerfriendly manner. Using this technique requires some expertise,
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
 American Political Science Review
, 2000
"... We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through ..."
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Cited by 147 (40 self)
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We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The reason for this discrepancy lies with the fact that the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise. In this paper, we adapt an existing algorithm, and use it to implement a generalpurpose, multiple imputation model for missing data. This algorithm is considerably faster and easier to use than the leading method recommended in the statistics literature. We also quantify the risks of current missing data practices, ...
On MCMC Sampling in Hierarchical Longitudinal Models
 Statistics and Computing
, 1998
"... this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameter ..."
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Cited by 14 (2 self)
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this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameters in a general linear mixed model may be updated in a single block, improving convergence and producing essentially independent draws from the posterior of the parameters of interest. We also investigate the value of blocking in nonGaussian mixed models, as well as in a class of binary response data longitudinal models. We illustrate the approaches in detail with three realdata examples.
Listwise deletion is evil: What to do about missing data in political science
 Paper Presented at the Annual Meeting of the American Political Science Association
, 1998
"... We propose a remedy to the substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing da ..."
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Cited by 14 (2 self)
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We propose a remedy to the substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of \multiple imputation, " but most researchers in our eld and other social sciences still use far inferior methods. Indeed, we demonstrate that the threats to validity from current missing data practices rival the biases from the much better known omitted variable problem. As it turns out, this discrepancy is not entirely our fault, as the computational algorithms used to apply the best multiple imputation models have been slow, di cult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise on the part of the user (even experts disagree on how to use them). In this paper, we adapt an existing algorithm, and use it to implement a generalpurpose, multiple imputation model for missing data. This algorithm is between 65 and
SequentiallyAllocated MergeSplit Sampler for Conjugate and Nonconjugate Dirichlet Process Mixture Models
, 2005
"... This paper proposes a new efficient mergesplit sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit using Markov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs and Gi ..."
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Cited by 12 (0 self)
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This paper proposes a new efficient mergesplit sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit using Markov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs and Gibbslike samplers for both conjugate and nonconjugate DPM models effectively update the model parameters, but can have difficulty in updating the clustering of the data. To overcome this deficiency, mergesplit samplers have been developed, but until now these have been limited to conjugate or conditionallyconjugate DPM models. This paper proposes a new MCMC sampler, called the sequentiallyallocated mergesplit (SAMS) sampler. The sampler borrows ideas from sequential importance sampling. Splits are proposed by sequentially allocating observations to one of two split components using allocation probabilities that condition on previously allocated data. The SAMS sampler is applicable to general nonconjugate DPM models as well as conjugate models. Further, the proposed sampler is substantially more efficient than existing conjugate and nonconjugate samplers.
Quasigeodesic neural learning algorithms over the orthogonal group: A tutorial
 Journal of Machine Learning Research
, 2005
"... The aim of this contribution is to present a tutorial on learning algorithms for a single neural layer whose connection matrix belongs to the orthogonal group. The algorithms exploit geodesics appropriately connected as piecewise approximate integrals of the exact differential learning equation. Th ..."
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Cited by 7 (0 self)
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The aim of this contribution is to present a tutorial on learning algorithms for a single neural layer whose connection matrix belongs to the orthogonal group. The algorithms exploit geodesics appropriately connected as piecewise approximate integrals of the exact differential learning equation. The considered learning equations essentially arise from the Riemanniangradientbased optimization theory with deterministic and diffusiontype gradient. The paper aims specifically at reviewing the relevant mathematics (and at presenting it in as much transparent way as possible in order to make it accessible to readers that do not possess a background in differential geometry), at bringing together modern optimization methods on manifolds and at comparing the different algorithms on a common machine learning problem. As a numerical casestudy, we consider an application to nonnegative independent component analysis, although it should be recognized that Riemannian gradient methods give rise to generalpurpose algorithms, by no means limited to ICArelated applications.
Multiple Hypothesis Testing by Clustering Treatment Effects
"... Multiple hypothesis testing and clustering have been the subject of extensive research in highdimensional inference, yet these problems usually have been treated separately. By defining true clusters in terms of shared parameter values, we could improve the sensitivity of individual tests, because ..."
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Cited by 6 (0 self)
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Multiple hypothesis testing and clustering have been the subject of extensive research in highdimensional inference, yet these problems usually have been treated separately. By defining true clusters in terms of shared parameter values, we could improve the sensitivity of individual tests, because more data bearing on the same parameter values are available. We develop and evaluate a hybrid methodology that uses clustering information to increase testing sensitivity and accommodates uncertainty in the true clustering. To investigate the potential efficacy of the hybrid approach, we first study a stylized example in which each object is evaluated with a standard z score but different objects are connected by shared parameter values. We show that there is increased testing power when the clustering is estimated sufficiently well. We next develop a modelbased analysis using a conjugate Dirichlet process mixture model. The method is general, but for specificity we focus attention on microarray gene expression data, to which both clustering and multiple testing methods are actively applied. Clusters provide the means for sharing information among genes, and the hybrid methodology averages over uncertainty in these clusters through Markov chain sampling. Simulations show that the hybrid method performs substantially better than other methods when clustering is heavy or moderate and performs well even under weak clustering. The proposed method is illustrated on microarray data from a study of the effects of aging on gene expression in heart tissue.
Comparing Hierarchical Models for Spatiotemporally Misaligned Data using the DIC Criterion
, 1999
"... this paper, we accomplish this comparison using the Deviance Information Criterion (DIC), a recently proposed generalization of the Akaike Information Criterion (AIC) designed for complex hierarchical model settings like ours. We investigate the use of the delta method for obtaining an approximate v ..."
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Cited by 6 (1 self)
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this paper, we accomplish this comparison using the Deviance Information Criterion (DIC), a recently proposed generalization of the Akaike Information Criterion (AIC) designed for complex hierarchical model settings like ours. We investigate the use of the delta method for obtaining an approximate variance estimate for DIC, in order to attach significance to apparent differences between models. We illustrate our approach using a spatially misaligned dataset relating a measure of traffic density to pediatric asthma hospitalizations in San Diego County, California.
Accelerating computation in Markov random field models for spatial data via structured MCMC
 Journal of Computational and Graphical Statistics
, 2003
"... ..."
Bayesian covariance matrix estimation using a mixture of decomposable graphical models. Unpublished manuscript
, 2005
"... Summary. Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data. We use ideas from Gaussian graphical models and model sele ..."
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Cited by 5 (2 self)
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Summary. Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data. We use ideas from Gaussian graphical models and model selection to construct a prior for the covariance matrix that is a mixture over all decomposable graphs, where a graph means the configuration of nonzero offdiagonal elements in the inverse of the covariance matrix. Our prior for the covariance matrix is such that the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We give empirical results that show the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs, both in identifying the correct decomposable graph and in more efficiently estimating the covariance matrix. The advantage is greatest when the number of observations is small relative to the dimension of the covariance matrix. Our method requires the number of decomposable graphs for each graph size. We show how to estimate these numbers using simulation and that the simulation results agree with analytic results when such results are known. We also show how