This paper is about Gaussian regression with random design, where the observations are i.i.d., it is known from Le Cam (1973, 1975 and 1986) that the rate of convergence of optimal estimators is closely connected to the metric structure of the parameter space with respect to the Hellinger distance. In particular, this metric structure essentially determines the risk when the loss function is a ...

As the influence of the internet continues to expand as a medium for communications and commerce, the threat from spammers, system attackers, and criminal enterprises has grown accordingly. This paper proposes a random effects logistic regression model to predict anomaly detection. Unlike the previous studies on anomaly detection, a random effects model was applied, which accommodates not only ...

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest...

Data, collected to model risk of an interesting event, often have a multilevel structure as patients are clustered within larger units, e.g. clinical centers. Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters. Model specification requires to decide which regressors have a non-negligible effect, and hence, ...

the fixed regression test-day model (frm) and random regression test-day model (rrm) for genetic evaluation of milk yield trait of dairy cattle in khorasan razavi province were studied. breeding values and genetic parameters of milk yield trait from two models were compared. a total of 164391 monthly test day milk records (three times milking per day) obtained from 19217 holstein cows distribut...

Background: This study investigates possible mechanisms that can explain the association between unemployment and smoking, that is a) unemployment increases smoking probability (causation), b) smoking increases the probability to become unemployed (selection), and c) differences in both smoking and unemployment probabilities trace back to differences in socio-economic position (common cause). M...