The estimation of quadratic functions of a multivariate normal mean is an inferential problem which, while being simple to state and often encountered in practice, leads to surprising complications both from frequentist and Bayesian points of view. The drawbacks of Bayesian inference using the...

In 1990, Hjort introduced nonparametric Bayes estimators of the cumulative distribution function and the cumulative hazard rate, based on type I censored data. Our aim in this paper is to study their large sample behaviour. Firstly, we develop a martingale structure for each estimator. Then, we...

We estimate the common density function of n i.i.d. observations, at a fixed point, over Sobolev classes of functions having regularity B. We prove that the optimal rate of convergence cannot be attained in adaptive estimation, i.e. uniformly over B in some interval B. A logarithmically slower...

Expansions of Penalized Likelihood Ratio Statistics and Consequences on Matching Priors for HPD Regions.

The data consists of multivariate failure times under right random censorship. By the kernel smoothing technique, convolutions of cumulative multivariate hazard functions suggest estimators of the so- called multivariate hazard functions. We establish strong i.i.d. representations and uniform...

Empirical demand systems that do not impose unreasonable restrictions on preferences are typically nonlinear. For empirical purposes, exact estimation of nonlinear equation systems for large data sets with more than a small number of equations has typically been limited by nonlinearities in the...

Using panel data for the Norwegian manufacturing, we revisit " the increasing returns to scale puzzle" for labour input. We consider the response of the input of white collar workers, blue collar workers, and blue collar worker hours to permanent changes in output. Permanent and temporary...

The proportional hazard model with unobserved heterogeneity gives the hazard function of a random variable conditional on covariates and a second random variable representing unobserved heterogeneity. This paper shows how to estimate the baseline hazard function and the distribution of the...

Given data from a sample of noisy curves in a nonlinear parametric regression model we consider nonparametric estimation of the model function and the parameters under certain structural assumptions. An algorithm for a consistent estimator is proposed and examples given.