We describe some recent approaches to likelihood based inference in the presence of nuisance parameters. Our approach is based on plotting the likelihood function and the p-value function, using recently developed third order approximations. Orthogonal parameters and adjustments to profile likelihood are also discussed. Connections to classical approaches of conditional and marginal inference a...

Suppose that U = (U(1), … , U(d)) has a Uniform ([0, 1](d)) distribution, that Y = (Y(1), … , Y(d)) has the distribution G on [Formula: see text], and let X = (X(1), … , X(d)) = (U(1)Y(1), … , U(d)Y(d)). The resulting class of distributions of X (as G varies over all distributions on [Formula: see text]) is called the Scale Mixture of Uniforms class of distributions, and the corresponding class...

The asymptotic distribution of the Markowitz portfolio, Σ̂μ̂, is derived, for the general case (assuming fourth moments of returns exist), and for the case of multivariate normal returns. The derivation allows for inference which is robust to heteroskedasticity and autocorrelation of moments up to order four. As a side effect, one can estimate the proportion of error in the Markowitz portfolio du...

Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwis...