Models with random effects/latent variables are widely used for capturing unobserved heterogeneity in multilevel/hierarchical data and account for associations in multivariate data. The estimation of those models becomes cumbersome as the number of latent variables increases due to high-dimensional integrations involved. Composite likelihood is a pseudo-likelihood that combines lower-order marg...

We derive a weighted least squares approximate restricted likelihood estimator for a kdimensional pth order autoregressive model with intercept, for which exact likelihood optimization is generally infeasible due to the parameter space which is complicated and highdimensional, involving pk2 parameters. The weighted least squares estimator has significantly reduced bias and mean squared error th...

In many randomized controlled trials the outcome of interest is a time to event, and one measures on each subject baseline covariates and time-dependent covariates until the subject either drops-out, the time to event is observed, or the end of study is reached. The goal of such a study is to assess the causal effect of the treatment on the survival curve. We present a targeted maximum likeliho...

Interpretable, Computationally Tractable Approximate Parameter Estimation for Corporate Defaults