133 research outputs found
Discussion of "Impact of Frequentist and Bayesian Methods on Survey Sampling Practice: A Selective Appraisal" by J. N. K. Rao
This comment emphasizes the importance of model checking and model fitting
when making inferences about finite population quantities. It also suggests the
value of using unit level models when making inferences for small
subpopulations, that is, "small area" analyses [arXiv:1108.2356].Comment: Published in at http://dx.doi.org/10.1214/11-STS346B the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Functional data analytic approach of modeling ECG T-wave shape to measure cardiovascular behavior
The T-wave of an electrocardiogram (ECG) represents the ventricular
repolarization that is critical in restoration of the heart muscle to a
pre-contractile state prior to the next beat. Alterations in the T-wave reflect
various cardiac conditions; and links between abnormal (prolonged) ventricular
repolarization and malignant arrhythmias have been documented. Cardiac safety
testing prior to approval of any new drug currently relies on two points of the
ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few
beats are measured. Using functional data analysis, a statistical approach
extracts a common shape for each subject (reference curve) from a sequence of
beats, and then models the deviation of each curve in the sequence from that
reference curve as a four-dimensional vector. The representation can be used to
distinguish differences between beats or to model shape changes in a subject's
T-wave over time. This model provides physically interpretable parameters
characterizing T-wave shape, and is robust to the determination of the endpoint
of the T-wave. Thus, this dimension reduction methodology offers the strong
potential for definition of more robust and more informative biomarkers of
cardiac abnormalities than the QT (or QT corrected) interval in current use.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS273 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
About item response theory models and how they work
This article is about FMCSA data and its analysis. The article responds to the two-part question: How does an Item Response Theory (IRT) model work differently . . . or better than any other model? The response to the first part is a careful, completely non-technical exposition of the fundamentals for IRT models. It differentiates IRT models from other models by providing the rationale underlying IRT modeling and by using graphs to illustrate two key properties for data items. The response to the second part of the question about superiority of an IRT model is, “it depends.” For FMCSA data, serious challenges arise from complexity of the data and from heterogeneity of the carrier industry. Questions are posed that will need to be addressed to determine the success of the actual model developed and of the scoring system
Tree-Based Methods: A Tool for Modeling Nonlinear Complex Relationships and Generating New Insights from Data
Our paper introduces tree-based methods, specifically classification and regression trees (CRT), to study student achievement. CRT allows data analysis to be driven by the data’s internal structure. Thus, CRT can model complex nonlinear relationships and supplement traditional hypothesis-testing approaches to provide a fuller picture of the topic being studied. Using Early Childhood Longitudinal Study-Kindergarten 2011 data as a case study, our research investigated predictors from students’ demographic backgrounds to ascertain their relationships to students’ academic performance and achievement gains in reading and math. In our study, CRT displays complex patterns between predictors and outcomes; more specifically, the patterns illuminated by the regression trees differ across the subject areas (i.e., reading and math) and between the performance levels and achievement gains. Through the use of real-world assessment datasets, this article demonstrates the strengths and limitations of CRT when analyzing student achievement data as well as the challenges. When achievement data such as achievement gains in our case study are not linearly strongly related to any continuous predictors, regression trees may make more accurate predictions than general linear models and produce results that are easier to interpret. Our study illustrates scenarios when CRT on achievement data is most appropriate and beneficial
A Case Study of Nonresponse Bias Analysis
Nonresponse bias is a widely prevalent problem for data collections. We
develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in
cross-sectional studies, and apply these steps to the Early Childhood
Longitudinal Study, Kindergarten Class of 2010-11. A key step is the
construction of indices of nonresponse bias based on proxy pattern-mixture
models for survey variables of interest. A novel feature is to characterize the
strength of evidence about nonresponse bias contained in these indices, based
on the strength of relationship of the characteristics in the nonresponse
adjustment with the key survey variables. Our NRBA incorporates missing at
random and missing not at random mechanisms, and all analyses can be done
straightforwardly with standard statistical software
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