We conceptualise life course pathways to entrepreneurship embedded in the resource-based view of the small enterprise. As a partial test, we propose a specific pathway structured by class and mediated by gender relations. This is operationalized employing 18 waves of the British Household Panel Survey and event history random effect logistic regression modelling. Findings suggest a primary, cla...

This paper proposes feasible nonparametric random effects estimators. Specifically, we propose feasible versions of the two estimators in Lin and Carroll (2000) and a modified version of the random effects estimator in Ullah and Roy (1998). Further, the consistency properties of these estimators are established.

In this paper, we address a strategic planning problem for a three-stage productiondistribution network. The problem under consideration is a singleitem, multi-supplier, multi-producer and multi-distributor production-distribution network with deterministic demand. The objective is to minimize the costs associated with production, transportation and inventory as well as capacity expansion costs...

Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets. We also appl...

Hooker et al. (2009) defined a paradoxical result as the attainment of a higher test score by changing answers from correct to incorrect and demonstrated that such results are unavoidable for maximum likelihood estimates in multidimensional item response theory. The potential for these results to occur leads to the undesirable possibility of a subject’s best answer being detrimental to them. Th...

The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. vVe describe the basic LaplaceMetropolis estimator for models without random effects. For models w...

Abstract: Nonlinear models have many applications in different areas such as pharmacokinetics and pharmacodynamics, and random effects are often included to take into account the correlation between observations taken within the same subject. In this context, we propose a bayesian analysis for heavy-tailed nonlinear mixed effects models, which may produce more robust estimates for the parameter...

We consider geostatistical models that allow the locations at which data are collected to be informative about the outcomes. Diggle et al. [2009] refer to this problem as preferential sampling, though we use the term informative sampling to highlight the relationship with the longitudinal data literature on informative observation times. In the longitudinal setting, joint models of the observat...

In human communication, speech understanding is greatly improvedby the bimodal acoustic-visual effect with respect to simple speech communication, in particular when the communication takes place in noisy environments. In this paper we propose a novel synchronization procedure between text and speech, to reduce the time consumption in the development of friendly audio--visual interfaces or auth...

A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization a...