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Longitudinal Data Analysis and Time Series Commons™
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- ANOVA (1)
- Almost sure weak convergence (1)
- Baseball home runs (1)
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- Cluster-randomized trial (1)
- Clustered data (1)
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- Shoulder tip pain study (1)
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Articles 1 - 4 of 4
Full-Text Articles in Longitudinal Data Analysis and Time Series
An Association Test For Ordinal Outcomes In Clustered Data With Informative Cluster Size, Hasika K. Wickrama Senevirathne, Sandipan Dutta
An Association Test For Ordinal Outcomes In Clustered Data With Informative Cluster Size, Hasika K. Wickrama Senevirathne, Sandipan Dutta
Mathematics & Statistics Faculty Publications
In cluster-correlated data, the number of observations in a cluster can be associated with the outcome from that cluster. This phenomenon is known as informative cluster size which can occur in cluster-randomized clinical trial data. Several studies have found that ignoring the issue of informative cluster size can produce biased results in the analysis of clustered data. Most of the existing methods for addressing informative cluster size are suited to continuous outcomes. However, ordinal outcomes and covariates are often encountered in clustered data obtained from large clinical studies. The existing methods for ordinal association testing in clustered data can produce …
Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner
Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner
Mathematics & Statistics Faculty Publications
This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …
New Approaches To Model Simulated Spatio-Temporal Moran's Index, Nhan Bu, Jennifer Lorio, Norou Diawara, Kumar Das, Lance Waller
New Approaches To Model Simulated Spatio-Temporal Moran's Index, Nhan Bu, Jennifer Lorio, Norou Diawara, Kumar Das, Lance Waller
Mathematics & Statistics Faculty Publications
The Moran's index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. However, when investigating data over a general area, a single global Moran statistic may not give a sufficient summary of the spread, behavior, features or latent surfaces shared by neighboring areas; rather, by partitioning the area and taking the Moran statistic of each divided subareas, we can discover patterns of the local neighbors not otherwise apparent. In this paper, we present a simulation experiment where the local Moran values are computed and a time variable is added to …
Testing Longitudinal Data By Logarithmic Quantiles, Manfred Denker, Lucia Tabacu
Testing Longitudinal Data By Logarithmic Quantiles, Manfred Denker, Lucia Tabacu
Mathematics & Statistics Faculty Publications
The shoulder tip pain study of Lumley [13] is re-investigated. It is shown that the new logarithmic quantile estimation (LQE) technique in [9] applies and behaves well under singular covariance structure and small sample sizes as in the shoulder tip pain study. The findings in [6] can be assured under weaker assumptions using a combination of LQE and an ANOVA type statistic. © 2014, Institute of Mathematical Statistics.