Quantile regression (QR) models are one of the methods for longitudinal data analysis. When responses seemto be skew and asymmetric due to outliers heavy-tails, QR may work suitably. This paper developes semi-parametric quantile model analyzing continuous ordinal mixed responses. The latent variable some threshold parameters used perform model’s part. error has Asymmetric Laplace (AL) distribut...

We study a joint model where logistic regression is applied to binary longitudinal data with mismeasured time-varying covariate that modeled using mechanistic nonlinear model. Multiple random effects are necessary characterize the trajectories of and response variable, leading high dimensional integral in likelihood. To account for computational challenge, we propose stochastic expectation-maxi...

During revision total knee arthroplasty, the joint line is frequently malpositioned, due to the disappearance of the anatomical landmarks following previous interventions. This leads to decreased clinical outcome and increased risk of re-intervention. Many methods have been proposed to restore the joint line, but none of them has shown itself to be reliable. We describe an accurate and precise ...

OBJECTIVE To determine if Q angle changes in magnitude from bilateral stance when compared to unilateral stance and then if they are significantly different changes related to the presence of patellofemoral joint pain. DESIGN Observational correlation. SETTING University biomechanics laboratory. PARTICIPANTS 60 Asymptomatic females and 12 females with patellofemoral joint pain. MAIN OUT...

We document asymmetric and time-varying features of dependence between the credit risks of global systemically important banks (G-SIBs) in the UK banking industry using a CDS dataset. We model the dependence of CDS spreads using a dynamic asymmetric copula. Comparing our model with traditional copula models, we find that they usually underestimate the probability of joint (or conditional) defau...

Likelihood-based marginal regression modelling for repeated, or otherwise clustered, categorical responses is computationally demanding. This is because the number of measures needed to describe the associations within a cluster increase geometrically with increasing cluster size. The proposed estimation methods typically describe the associations using odds ratios, which result in computationa...