Browsing by Author "Chen, Min"
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Item Community structure of deep-sea bivalve mollusks from the northern Gulf of Mexico(Texas A&M University, 2004-09-30) Chen, MinDensity, species diversity, species richness, and evenness of bivalve mollusks were measured in the deep (0.2km to 3.7km) northern Gulf of Mexico to describe the community structure of benthic bivalve mollusks. Density decreased gradually from shallow continental slope depths, with remarkably high values in the Mississippi canyon, to the deepest sites. Diversity of bivalve mollusks increased from shallow continental slope depths, with low values in the Mississippi canyon, to a maximum at intermediate depths (1-2km), followed by a decrease down to the deepest locations (3.7km). Nine distinct groups were formed on the basis of the similarity in species composition. The pattern varied more abruptly on the slope compared to the deeper depths, possibly due to steeper gradients in physical variables. ANOVA indicated that the density of bivalve mollusks was not significantly different at different depths, was not significantly different on different transects, was not significantly different between basin and non-basin, but was significantly different in canyon and non-canyon locations. Similar distinctions were observed in diversity, except that basins were lower than non-basins. The patterns observed reflect the intense elevated input of terrigenous sediments accompanied by high surface-water plankton production from the Mississippi River to the north central gulf.Item Inevitable disappointment and decision making based on forecasts(2006) Chen, Min; Dyer, JamesItem Modeling covariance structure in unbalanced longitudinal data(2009-05-15) Chen, MinModeling covariance structure is important for efficient estimation in longitudinal data models. Modified Cholesky decomposition (Pourahmadi, 1999) is used as an unconstrained reparameterization of the covariance matrix. The resulting new parameters have transparent statistical interpretations and are easily modeled using covariates. However, this approach is not directly applicable when the longitudinal data are unbalanced, because a Cholesky factorization for observed data that is coherent across all subjects usually does not exist. We overcome this difficulty by treating the problem as a missing data problem and employing a generalized EM algorithm to compute the ML estimators. We study the covariance matrices in both fixed-effects models and mixed-effects models for unbalanced longitudinal data. We illustrate our method by reanalyzing Kenwards (1987) cattle data and conducting simulation studies.