Pedestrianand cyclistinvolved crashes at intersections remain a major concern in China due to high ratio of fatal injuries and fatalities. This paper develops the random parameters negative binominal model to determine the relationship between potential contributory factors and crash frequency involving pedestrians and bicyclists at intersection locations and identify the significant risk facto...

Consider a continuous random pair (X, Y) whose dependence is characterized by an extreme-value copula with Pickands dependence function A. When the marginal distributions of X and Y are known, several consistent estimators of A are available. Most of them are variants of the estimators due to Pickands [Bull. In this paper, rank-based versions of these esti-mators are proposed for the more commo...

normal 0 false false false en-us x-none ar-sa microsoftinternetexplorer4 background: e pilepsy is a common, chronic neurological disorder that affects more than 40 million people worldwide. epilepsy is characterized by interictal and ictal functional disturbances. the presence of interictal epileptiform discharges (ieds) can help to confirm a clinical diagnosis of epilepsy, and their location a...

abstract this paper carried out to study determinants of willingness to pay and ecotourism value of tandoreh natioral park using cross section data of 144 households of dargaz and ghochan and contingent value method and logit model in 2008. results showed that age and entrance offer have negative effect and income have positive effect on willingness to pay for ecotourism in tandoreh natioral pa...

Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model by introducing latent random variables. The first approach is effective for parameter estimation, but leaves one without a formal model for the data with which to asse...

Marginal structural models (MSMs) are becoming increasingly popular as a tool to make causal inference from longitudinal data. Unlike standard regression models, MSMs can adjust for time-dependent observed confounders while avoiding the bias due to the adjustment for covariates affected by the treatment. Despite their theoretical appeal, a main practical difficulty of MSMs is the required estim...

Emerging adulthood researchers are often interested in the effects of developmental tasks. The majority of transitions that occur during the period of early/emerging adulthood are not randomized; therefore, their effects on developmental trajectories are subject to potential bias due to confounding. Traditionally, confounding has been addressed using regression adjustment; however, there are vi...

Semi-parametrically specified models for multivariate, longitudinal, clustered, multilevel, and other hierarchical data, particularly for non-Gaussian outcomes, are ubiquitous because their parameters can most often be conveniently estimated using the important class of generalized estimating equations (GEE). The focus here is on marginal models, to be understood as models that condition neithe...

The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of t...