Motivated by the availability of informative covariates or features when modeling the network structure for multivariate binary data, we propose a sparse covariate dependent Ising model to study the conditional dependency patterns of the binary variables and their relationship with the covariates. Our model relaxes the i.i.d. assumption of the network data commonly used in the literature and na...

The estimation of the Pareto index in presence of covariate information is discussed. The Pareto index is modelled as a function of the explanatory variables and hence measures the tail heaviness of the conditional distribution of the response variable given this covariate information. The original response data are transformed in order to obtain generalized residuals, possessing a common Paret...

Multi-state models are frequently applied to describe transitions over time between three states: healthy, not healthy and death. The three-state model can be used to estimate life expectancies in health and ill health. In this article, continuous-time Markov models are specified for the transitions between the three states. Transition intensities are regressed on age as a time-dependent covari...

We propose semiparametric methods for estimating the effect of a time-dependent covariate on treatment-free survival. The data structure of interest consists of a longitudinal sequence of measurements and a potentially censored survival time. The factor of interest is time-dependent. Treatment-free survival is of interest and is dependently censored by the receipt of treatment. Patients may be ...

Abstract Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variab...

Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions. Unfortunately, common methods for addressing covariate shift by trying to remove the bias between training and testing distributions using importance weighting often provide poor performance guarantees in theory and unreliable predi...

In this paper, we propose a covariate-adjusted nonlinear regression model. In this model, both the response and predictors can only be observed after being distorted by some multiplicative factors. Because of nonlinearity, existing methods for the linear setting cannot be directly employed. To attack this problem, we propose estimating the distorting functions by nonparametrically regressing th...

Various methods to control the influence of a covariate on a response variable are compared. In particular, ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal-Wallis (K-W) tests on covariate-adjusted residuals and analysis of covariance (ANCOVA) are compared. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set ignoring th...