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Longitudinal Data Analysis and Time Series Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
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- Asymptotic efficiency; Conditional score method; Functional modeling; Measurement error; Longitudinal data; Transition models (1)
- Bayesian model averaging; Missing data; Imputation; Air pollution; Particulate matter (1)
- Latent variable; particulate matter; Laird-Ware model; random intercept - random slope model; semiparametric regression; penalized spline (1)
Articles 1 - 8 of 8
Full-Text Articles in Longitudinal Data Analysis and Time Series
Bayesian Model Averaging For Clustered Data: Imputing Missing Daily Air Pollution Concentration, Howard H. Chang, Francesca Dominici, Roger D. Peng
Bayesian Model Averaging For Clustered Data: Imputing Missing Daily Air Pollution Concentration, Howard H. Chang, Francesca Dominici, Roger D. Peng
Johns Hopkins University, Dept. of Biostatistics Working Papers
The presence of missing observations is a challenge in statistical analysis especially when data are clustered. In this paper, we develop a Bayesian model averaging (BMA) approach for imputing missing observations in clustered data. Our approach extends BMA by allowing the weights of competing regression models for missing data imputation to vary between clusters while borrowing information across clusters in estimating model parameters. Through simulation and cross-validation studies, we demonstrate that our approach outperforms the standard BMA imputation approach where model weights are assumed to be the same for all clusters. We then apply our proposed method to a national …
Spatial Misalignment In Time Series Studies Of Air Pollution And Health Data, Roger D. Peng, Michelle L. Bell
Spatial Misalignment In Time Series Studies Of Air Pollution And Health Data, Roger D. Peng, Michelle L. Bell
Johns Hopkins University, Dept. of Biostatistics Working Papers
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter. One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of …
Space-Time Regression Modeling Of Tree Growth Using The Skew-T Distribution, Farouk S. Nathoo
Space-Time Regression Modeling Of Tree Growth Using The Skew-T Distribution, Farouk S. Nathoo
COBRA Preprint Series
In this article we present new statistical methodology for the analysis of repeated measures of spatially correlated growth data. Our motivating application, a ten year study of height growth in a plantation of even-aged white spruce, presents several challenges for statistical analysis. Here, the growth measurements arise from an asymmetric distribution, with heavy tails, and thus standard longitudinal regression models based on a Gaussian error structure are not appropriate. We seek more flexibility for modeling both skewness and fat tails, and achieve this within the class of skew-elliptical distributions. Within this framework, robust space-time regression models are formulated using random …
A Functional Random Effects Model For Flexible Assessment Of Susceptibility In Longitudinal Designs, Brent A. Coull
A Functional Random Effects Model For Flexible Assessment Of Susceptibility In Longitudinal Designs, Brent A. Coull
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
"%Qls Sas Macro: A Sas Macro For Analysis Of Longitudinal Data Using Quasi-Least Squares"., Hanjoo Kim, Justine Shults
"%Qls Sas Macro: A Sas Macro For Analysis Of Longitudinal Data Using Quasi-Least Squares"., Hanjoo Kim, Justine Shults
UPenn Biostatistics Working Papers
Quasi-least squares (QLS) is an alternative computational approach for estimation of the correlation parameter in the framework of generalized estimating equations (GEE). QLS overcomes some limitations of GEE that were discussed in Crowder (Biometrika 82 (1995) 407-410). In addition, it allows for easier implementation of some correlation structures that are not available for GEE. We describe a user written SAS macro called %QLS, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. %QLS also computes the lower and upper boundaries of the correlation parameter for analysis of …
Joint Spatial Modeling Of Recurrent Infection And Growth With Processes Under Intermittent Observation, Farouk S. Nathoo
Joint Spatial Modeling Of Recurrent Infection And Growth With Processes Under Intermittent Observation, Farouk S. Nathoo
COBRA Preprint Series
In this article we present new statistical methodology for longitudinal studies in forestry where trees are subject to recurrent infection and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations which poses computational difficulties for spatiotemporal modeling. A joint model for infection and growth is developed; wherein, a …
On The Designation Of The Patterned Associations For Longitudinal Bernoulli Data: Weight Matrix Versus True Correlation Structure?, Hanjoo Kim, Joseph M. Hilbe, Justine Shults
On The Designation Of The Patterned Associations For Longitudinal Bernoulli Data: Weight Matrix Versus True Correlation Structure?, Hanjoo Kim, Joseph M. Hilbe, Justine Shults
UPenn Biostatistics Working Papers
Due to potential violation of standard constraints for the correlation for binary data, it has been argued recently that the working correlation matrix should be viewed as a weight matrix that should not be confused with the true correlation structure. We propose two arguments to support our view to the contrary for the first-order autoregressive AR(1) correlation matrix. First, we prove that the standard constraints are not unduly restrictive for the AR(1) structure that is plausible for longitudinal data; furthermore, for the logit link function the upper boundary value only depends on the regression parameter and the change in covariate …