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Full-Text Articles in Physical Sciences and Mathematics
A Reduced Bias Method Of Estimating Variance Components In Generalized Linear Mixed Models, Elizabeth A. Claassen
A Reduced Bias Method Of Estimating Variance Components In Generalized Linear Mixed Models, Elizabeth A. Claassen
Department of Statistics: Dissertations, Theses, and Student Work
In small samples it is well known that the standard methods for estimating variance components in a generalized linear mixed model (GLMM), pseudo-likelihood and maximum likelihood, yield estimates that are biased downward. An important consequence of this is that inferences on fixed effects will have inflated Type I error rates because their precision is overstated. We introduce a new method for estimating parameters in GLMMs that applies a Firth bias adjustment to the maximum likelihood-based GLMM estimating algorithm. We apply this technique to one- and two-treatment logistic regression models with a single random effect. We show simulation results that demonstrate …
New Statistical Methods For Analysis Of Historical Data From Wildlife Populations, Trevor Hefley
New Statistical Methods For Analysis Of Historical Data From Wildlife Populations, Trevor Hefley
Department of Statistics: Dissertations, Theses, and Student Work
Wildlife biologists, many times with the help of ordinary citizens, have developed and maintained long-term datasets for monitoring the status of wildlife populations. These datasets can range from a collection of citizen-reported sightings of a rare species, to datasets collected by biologists using standardized methods. The commonality is that these datasets span a temporal and spatial scale that is beyond the scope of most scientific studies. Ensuring the continued persistence of wildlife populations requires predictions of the impact of human actions. Regardless if the predictions are quantitative or qualitative, the best we can do is use the past data to …
Native Insect Herbivory Limits Population Growth Rate Of A Non-Native Thistle, James O. Eckberg, Brigitte Tenhumberg, Svata M. Louda
Native Insect Herbivory Limits Population Growth Rate Of A Non-Native Thistle, James O. Eckberg, Brigitte Tenhumberg, Svata M. Louda
Brigitte Tenhumberg Papers
The influence of native fauna on non-native plant population growth, size, and distribution is not well documented. Previous studies have shown that native insects associated with tall thistle (Cirsium altissimum) also feed on the leaves, stems, and flower heads of the Eurasian congener Cirsium vulgare, thus limiting individual plant performance. In this study, we tested the effects of insect herbivores on the population growth rate of C. vulgare. We experimentally initiated invasions by adding seeds at four unoccupied grassland sites in eastern Nebraska, USA, and recorded plant establishment, survival, and reproduction. Cumulative foliage and floral herbivory …
Confidence Intervals For Ranks Of Age-Adjusted Rates Across States Or Counties, Shunpu Zhang, Jun Luo, Li Zhu, David G. Stinchcomb, Dave Campbell, Ginger Carter, Scott Gilkeson, Eric J. Feuer
Confidence Intervals For Ranks Of Age-Adjusted Rates Across States Or Counties, Shunpu Zhang, Jun Luo, Li Zhu, David G. Stinchcomb, Dave Campbell, Ginger Carter, Scott Gilkeson, Eric J. Feuer
Department of Statistics: Faculty Publications
Health indices provide information to the general public on the health condition of the community. They can also be used to inform the government’s policy making, to evaluate the effect of a current policy or healthcare program, or for program planning and priority setting. It is a common practice that the health indices across different geographic units are ranked and the ranks are reported as fixed values. We argue that the ranks should be viewed as random and hence should be accompanied by an indication of precision (i.e., the confidence intervals). A technical difficulty in doing so is how to …
Parametric And Nonparametric Statistical Methods For Genomic Selection Of Traits With Additive And Epistatic Genetic Architectures, Reka Howard, Alicia L. Carriquiry, William D. Beavis
Parametric And Nonparametric Statistical Methods For Genomic Selection Of Traits With Additive And Epistatic Genetic Architectures, Reka Howard, Alicia L. Carriquiry, William D. Beavis
Department of Statistics: Faculty Publications
Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, …