Home About us Contact
|
Home About us Contact | ||
|
|
||
Real Data Application (real + data_application)
Selected AbstractsValidation of Group Domain Score Estimates Using a Test of DomainJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 2 2006Mary Pommerich Domain scores have been proposed as a user-friendly way of providing instructional feedback about examinees' skills. Domain performance typically cannot be measured directly; instead, scores must be estimated using available information. Simulation studies suggest that IRT-based methods yield accurate group domain score estimates. Because simulations can represent best-case scenarios for methodology, it is important to verify results with a real data application. This study administered a domain of elementary algebra (EA) items created from operational test forms. An IRT-based group-level domain score was estimated from responses to a subset of taken items (comprised of EA items from a single operational form) and compared to the actual observed domain score. Domain item parameters were calibrated both using item responses from the special study and from national operational administrations of the items. The accuracy of the domain score estimates were evaluated within schools and across school sizes for each set of parameters. The IRT-based domain score estimates typically were closer to the actual domain score than observed performance on the EA items from the single form. Previously simulated findings for the IRT-based domain score estimation procedure were supported by the results of the real data application. [source] A Bayesian Hierarchical Model for Classification with Selection of Functional PredictorsBIOMETRICS, Issue 2 2010Hongxiao Zhu Summary In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis,Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification. [source] Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic TestsBIOMETRICS, Issue 2 2010Qing Lu Summary Current ongoing genome-wide association (GWA) studies represent a powerful approach to uncover common unknown genetic variants causing common complex diseases. The discovery of these genetic variants offers an important opportunity for early disease prediction, prevention, and individualized treatment. We describe here a method of combining multiple genetic variants for early disease prediction, based on the optimality theory of the likelihood ratio (LR). Such theory simply shows that the receiver operating characteristic (ROC) curve based on the LR has maximum performance at each cutoff point and that the area under the ROC curve so obtained is highest among that of all approaches. Through simulations and a real data application, we compared it with the commonly used logistic regression and classification tree approaches. The three approaches show similar performance if we know the underlying disease model. However, for most common diseases we have little prior knowledge of the disease model and in this situation the new method has an advantage over logistic regression and classification tree approaches. We applied the new method to the type 1 diabetes GWA data from the Wellcome Trust Case Control Consortium. Based on five single nucleotide polymorphisms, the test reaches medium level classification accuracy. With more genetic findings to be discovered in the future, we believe a predictive genetic test for type 1 diabetes can be successfully constructed and eventually implemented for clinical use. [source] Mixture Modeling for Genome-Wide Localization of Transcription FactorsBIOMETRICS, Issue 1 2007Sündüz Kele Summary Chromatin immunoprecipitation followed by DNA microarray analysis (ChIP-chip methodology) is an efficient way of mapping genome-wide protein,DNA interactions. Data from tiling arrays encompass DNA,protein interaction measurements on thousands or millions of short oligonucleotides (probes) tiling a whole chromosome or genome. We propose a new model-based method for analyzing ChIP-chip data. The proposed model is motivated by the widely used two-component multinomial mixture model of de novo motif finding. It utilizes a hierarchical gamma mixture model of binding intensities while incorporating inherent spatial structure of the data. In this model, genomic regions belong to either one of the following two general groups: regions with a local protein,DNA interaction (peak) and regions lacking this interaction. Individual probes within a genomic region are allowed to have different localization rates accommodating different binding affinities. A novel feature of this model is the incorporation of a distribution for the peak size derived from the experimental design and parameters. This leads to the relaxation of the fixed peak size assumption that is commonly employed when computing a test statistic for these types of spatial data. Simulation studies and a real data application demonstrate good operating characteristics of the method including high sensitivity with small sample sizes when compared to available alternative methods. [source] Idiot's Bayes,Not So Stupid After All?INTERNATIONAL STATISTICAL REVIEW, Issue 3 2001David J. Hand Summary Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective. Résumé La tradition veunt qu'une règle très simple assumant l'independance des variables prédictives. une hypothèse fausse dans la plupart des cas, peut être très efficace, souvent même plus efficace qu'une méthode plus sophistiquée en ce qui concerne l'attribution de classes a un groupe d'objets. A ce sujet, nous examinons les preuves empiriques, et les preuves théoriques, e'est-a-dire les raisons pour lesquelles cette simple règle pourrait faciliter le processus de tri. [source] Skills Diagnosis Using IRT-Based Latent Class ModelsJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 4 2007Louis A. Roussos This article describes a latent trait approach to skills diagnosis based on a particular variety of latent class models that employ item response functions (IRFs) as in typical item response theory (IRT) models. To enable and encourage comparisons with other approaches, this description is provided in terms of the main components of any psychometric approach: the ability model and the IRF structure; review of research on estimation, model checking, reliability, validity, equating, and scoring; and a brief review of real data applications. In this manner the article demonstrates that this approach to skills diagnosis has built a strong initial foundation of research and resources available to potential users. The outlook for future research and applications is discussed with special emphasis on a call for pilot studies and concomitant increased validity research. [source] A Latent Model to Detect Multiple Clusters of Varying SizesBIOMETRICS, Issue 4 2009Minge Xie Summary This article develops a latent model and likelihood-based inference to detect temporal clustering of events. The model mimics typical processes generating the observed data. We apply model selection techniques to determine the number of clusters, and develop likelihood inference and a Monte Carlo expectation,maximization algorithm to estimate model parameters, detect clusters, and identify cluster locations. Our method differs from the classical scan statistic in that we can simultaneously detect multiple clusters of varying sizes. We illustrate the methodology with two real data applications and evaluate its efficiency through simulation studies. For the typical data-generating process, our methodology is more efficient than a competing procedure that relies on least squares. [source] Assessment of Agreement under Nonstandard Conditions Using Regression Models for Mean and VarianceBIOMETRICS, Issue 1 2006Pankaj K. Choudhary Summary The total deviation index of Lin (2000, Statistics in Medicine19, 255,270) and Lin et al. (2002, Journal of the American Statistical Association97, 257,270) is an intuitive approach for the assessment of agreement between two methods of measurement. It assumes that the differences of the paired measurements are a random sample from a normal distribution and works essentially by constructing a probability content tolerance interval for this distribution. We generalize this approach to the case when differences may not have identical distributions,a common scenario in applications. In particular, we use the regression approach to model the mean and the variance of differences as functions of observed values of the average of the paired measurements, and describe two methods based on asymptotic theory of maximum likelihood estimators for constructing a simultaneous probability content tolerance band. The first method uses bootstrap to approximate the critical point and the second method is an analytical approximation. Simulation shows that the first method works well for sample sizes as small as 30 and the second method is preferable for large sample sizes. We also extend the methodology for the case when the mean function is modeled using penalized splines via a mixed model representation. Two real data applications are presented. [source] | ||||||||||