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Full-Text Articles in Science and Technology Studies
Robust Designs For Poisson Regression Models, J M. Mcgree, J A. Eccleston
Robust Designs For Poisson Regression Models, J M. Mcgree, J A. Eccleston
Faculty of Engineering and Information Sciences - Papers: Part A
We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given cer- tain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology con- structs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application …
Estimation In Autoregressive Population Models, Thomas Suesse
Estimation In Autoregressive Population Models, Thomas Suesse
Faculty of Engineering and Information Sciences - Papers: Part A
Autoregressive (AR) models for spatial and social interaction have been proposed by many authors. A sample of units is obtained and the model is applied to this sample. Estimation methods such as the maximum likelihood method (ML) have been employed and investigated in the literature. The main assumption is that a response depends on other responses, when these units interact. Some of those units will be in the sample and some in the non-sample. Therefore the model should apply to the whole population rather to the sample only. Under such a population model, the marginal model for the responses of …
Exponential-Family Random Graph Models For Valued Networks, Pavel N. Krivitsky
Exponential-Family Random Graph Models For Valued Networks, Pavel N. Krivitsky
Faculty of Engineering and Information Sciences - Papers: Part A
Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a- friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM …
Marginalized Exponential Random Graph Models, Thomas F. Suesse
Marginalized Exponential Random Graph Models, Thomas F. Suesse
Faculty of Engineering and Information Sciences - Papers: Part A
Exponential random graph models (ERGMs) are a popular tool for modeling social networks representing relational data, such as working relationships or friendships. Data on exogenous variables relating to participants in the network, such as gender or age, are also often collected. ERGMs allow modeling of the effects of such exogenous variables on the joint distribution, specified by the ERGM, but not on the marginal probabilities of observing a relationship. In this article, we consider an approach to modeling a network that uses an ERGM for the joint distribution of the network, but then marginally constrains the fit to agree with …
Estimation Of Breeding Values For Mean And Dispersion, Their Variance And Correlation Using Double Hierarchical Generalized Linear Models, M Felleki, D Lee, Y Lee, A R. Gilmour, L Ronnegard
Estimation Of Breeding Values For Mean And Dispersion, Their Variance And Correlation Using Double Hierarchical Generalized Linear Models, M Felleki, D Lee, Y Lee, A R. Gilmour, L Ronnegard
Faculty of Engineering and Information Sciences - Papers: Part A
The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of …