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226
Principles and practice in reporting structural equation analyses
 PSYCHOLOGICAL METHODS
, 2002
"... Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of ident ..."
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Cited by 36 (0 self)
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Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodnessoffit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.
Applications of randomeffects patternmixture models for missing data in longitudinal studies. Psychol Methods
, 1997
"... Randomeffects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the randomeffects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of randomeffects patternmix ..."
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Cited by 26 (4 self)
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Randomeffects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the randomeffects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of randomeffects patternmixture models to further handle and describe the influence of missing data in longitudinal studies. For this approach, subjects are first divided into groups depending on their missingdata pattern and then variables based on these groups are used as model covariates. Tn this way, researchers are able to examine the effect of missingdata patterns on the outcome (or outcomes) of interest. Furthermore, overall estimates can be obtained by averaging over the missingdata patterns. A psychiatric clinical trials data set is used to illustrate the randomeffects patternmixture approach to longitudinal data analysis with missing data. Longitudinal studies occupy an important role in psychological and psychiatric research. In these studies the same individuals are repeatedly measured on a number of important variables over a series of timepoints. As an example, a longitudinal design is often used to determine whether a particular therapeutic agent can produce changes in clinical status over the course of an illness. Another application for the longitudinal study is to assess potential indicators of a change, in the subject's clinical status; for example, the assessment of whether drug plasma level measurements indicate clinical outcome. Even in wellcontrolled situations, missing data invariably occur in longitudinal studies. Subjects can be
Collaborative filtering and the missing at random assumption. To be published
 in Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence. 2007
, 2007
"... Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present ..."
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Cited by 25 (4 self)
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Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a user study in which we collect a random sample of ratings from current users of an online radio service. An analysis of the rating data collected in the study shows that the sample of random ratings has markedly different properties than ratings of userselected songs. When asked to report on their own rating behaviour, a large number of users indicate they believe their opinion of a song does affect whether they choose to rate that song, a violation of the MAR condition. Finally, we present experimental results showing that incorporating an explicit model of the missing data mechanism can lead to significant improvements in prediction performance on the random sample of ratings. 1
On Structural Equation Modeling with Data that are not Missing Completely at Random
 Psychometrika
, 1987
"... A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a p ..."
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Cited by 24 (2 self)
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A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior. Key words: maximum likelihood, ignorability, selectivity, factor analysis, factorial invariance,
Poststratification Into Many Categories Using Hierarchical Logistic Regression
, 1997
"... A standard method for correcting for unequal sampling probabilities and nonresponse in sample surveys is poststratification: that is, dividing the population into several categories, estimating the distribution of responses in each category, and then counting each category in proportion to its size ..."
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Cited by 23 (12 self)
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A standard method for correcting for unequal sampling probabilities and nonresponse in sample surveys is poststratification: that is, dividing the population into several categories, estimating the distribution of responses in each category, and then counting each category in proportion to its size in the population. We consider poststratification as a general framework that includes many weighting schemes used in survey analysis (see Little, 1993). We construct a hierarchical logistic regression model for the mean of a binary response variable conditional on poststratification cells. The hierarchical model allows us to fit many more cells than is possible using classical methods, and thus to include much more populationlevel information, while at the same time including all the information used in standard survey sampling inferences. We are thus combining the modeling approach often used in smallarea estimation with the population information used in poststratification. We apply the...
Not Asked Or Not Answered: Multiple Imputation for Multiple Surveys
 Journal of the American Statistical Association
, 1998
"... We present a method of analyzing a series of independent crosssectional surveys in which some questions are not answered in some surveys and some respondents do not answer some of the questions posed. The method is also applicable to a single survey in which different questions are asked, or differ ..."
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Cited by 21 (8 self)
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We present a method of analyzing a series of independent crosssectional surveys in which some questions are not answered in some surveys and some respondents do not answer some of the questions posed. The method is also applicable to a single survey in which different questions are asked, or different sampling methods used, in different strata or clusters. Our method involves multiplyimputing the missing items and questions by adding to existing methods of imputation designed for single surveys a hierarchical regression model that allows covariates at the individual and survey levels. Information from survey weights is exploited by including in the analysis the variables on which the weights were based, and then reweighting individual responses (observed and imputed) to estimate population quantities. We also develop diagnostics for checking the fit of the imputation model based on comparing imputed to nonimputed data. We illustrate with the example that motivated this project  a ...
Parameter Estimation in Bayesian Networks from Incomplete Databases
, 1998
"... Current methods to learn Bayesian Networks from incomplete databases share the common assumption that the unreported data are missing at random. This paper describes a method  called Bound and Collapse (bc)  to learn Bayesian Networks from incomplete databases which allows the analyst to effic ..."
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Cited by 20 (3 self)
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Current methods to learn Bayesian Networks from incomplete databases share the common assumption that the unreported data are missing at random. This paper describes a method  called Bound and Collapse (bc)  to learn Bayesian Networks from incomplete databases which allows the analyst to efficiently integrate information provided by the observed data and exogenous knowledge about the pattern of missing data. bc starts by bounding the set of estimates consistent with the available information and then collapses the resulting set to a point estimate via a convex combination of the extreme points, with weights depending on the assumed pattern of missing data. Experiments comparing bc to Gibbs Samplings are provided. Keywords: Bayesian Inference
Learning Reliable Classifiers from Small or Incomplete Data Sets: the Naive Credal Classifier 2
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... In this paper, the naive credal classifier, which is a setvalued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in t ..."
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Cited by 18 (12 self)
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In this paper, the naive credal classifier, which is a setvalued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, by issuing setvalued classifications, NCC2 is able to isolate and properly deal with instances that are hard to classify (on which naive Bayes accuracy drops considerably), and to perform as well as naive Bayes on the other instances. The experiments point to a general problem: they show that with missing values, empirical evaluations may not reliably estimate the accuracy of a traditional classifier, such as naive Bayes. This phenomenon adds even more value to the robust approach to classification implemented by NCC2.
A Bayesian formulation of exploratory data analysis and goodnessoffit testing
, 2003
"... Exploratory data analysis (EDA) and Bayesian inference (or, more generally, complex statistical modeling)which are generally considered as unrelated statistical paradigmscan be particularly eective in combination. In this paper, we present a Bayesian framework for EDA based on posterior predict ..."
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Cited by 17 (9 self)
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Exploratory data analysis (EDA) and Bayesian inference (or, more generally, complex statistical modeling)which are generally considered as unrelated statistical paradigmscan be particularly eective in combination. In this paper, we present a Bayesian framework for EDA based on posterior predictive checks. We explain how posterior predictive simulations can be used to create reference distributions for EDA graphs, and how this approach resolves some theoretical problems in Bayesian data analysis. We show how the generalization of Bayesian inference to include replicated data y and replicated parameters follows a long tradition of generalizations in Bayesian theory.