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Full-Text Articles in Physical Sciences and Mathematics
A Comparison Of Methods For Longitudinal Analysis With Missing Data, James Algina, H. J. Keselman
A Comparison Of Methods For Longitudinal Analysis With Missing Data, James Algina, H. J. Keselman
Journal of Modern Applied Statistical Methods
In a longitudinal two-group randomized trials design, also referred to as randomized parallel-groups design or split-plot repeated measures design, the important hypothesis of interest is whether there are differential rates of change over time, that is, whether there is a group by time interaction. Several analytic methods have been presented in the literature for testing this important hypothesis when data are incomplete. We studied these methods for the case in which the missing data pattern is non-monotone. In agreement with earlier work on monotone missing data patterns, our results on bias, sampling variability, Type I error and power support the …
A Comparison Of Geostatistical And Spatial Autoregressive Approaches For Dealing With Spatially Correlated Residuals In Regression Analysis For Precision Agriculture Applications, Ignacio Colonna, Matías Ruffo, Germán Bollero, Don Bullock
A Comparison Of Geostatistical And Spatial Autoregressive Approaches For Dealing With Spatially Correlated Residuals In Regression Analysis For Precision Agriculture Applications, Ignacio Colonna, Matías Ruffo, Germán Bollero, Don Bullock
Conference on Applied Statistics in Agriculture
Regressions such as Grain yield=f(soil,landscape) are frequently reported in precision agriculture research, and are typically computed using conventional OLS methods, implicitly ignoring spatial correlation of the residuals. This oversight can have a marked effect on the final conclusions derived from these regressions. A further issue is, which approach should be used to account for this problem? We investigated this question using a 2 year data set that includes sitespecific soil and topographic information and soybean yields and compare regression results from direct covariance representation and spatial autoregressive approaches. Our results show that the coefficients from both spatial approaches are in …
Comparing Analyses Of Unbalanced Split-Plot Experiments, Christina D. Smith, Dallas E. Johnson
Comparing Analyses Of Unbalanced Split-Plot Experiments, Christina D. Smith, Dallas E. Johnson
Conference on Applied Statistics in Agriculture
Several procedures for constructing confidence intervals and testing hypotheses about fixed effects in unbalanced split-plot experiments have previously been presented and discussed by Remmenga and Johnson. They recommended a few of the procedures they considered as useful and reliable procedures. Since the advent of the SAS® MIXED procedure, mixed model analyses with REML estimates of the variance components are easily accessible to researchers. This paper compares the analysis of unbalanced split-plot experiments using mixed model procedures with REML estimates of the variance components to the previously established procedures by means of additional simulation studies.