The adventures of Roderick Random.

Mode of access: Internet.

The adventures of Roderick Random /

Added t.-p., illus.

De Callisthene Olynthio et Pseudo-Callisthene qui dicitur.

Pages extracted from programs from a university in Leipzig 1838-1842.

Auswahl pseudo-Davidischer Psalmen [microform] : arabisch und deutsch /

Microfilm.

Die pseudo-cannel-kohle.

Thesis (doctoral)--

Untersuchungen uber Pseudo-Ignatius : ein Beitrag zur Geschichte einer litterarischen Falschung.

Thesis (doctoral)--Universitat Marburg.

Random vibration : decrease corporate cost increase fleet readiness /

NAVELEX final technical report, December 1978.

Propagation of spherical waves in a weak random medium.

"NsG 24-59. Sponsored by National Aeronautics and Space Administration."

Response of a cantilever beam to non-stationary and stationary random excitation

Mode of access: Internet.

Roderick Random.

Caption title: The adventures of Roderick Random.

Random recursion

Thesis (Ph.D.)--University of Washington, 2016-06

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.