We review gene mapping, or inference for quantitative trait loci, in the context of recent research in semi-parametric and non-parametric inference for mixture models. Gene mapping studies the relationship between a phenotypic trait and inherited genotype. Semi-parametric gene mapping using the exponential tilt covers most standard exponential families and improves estimation of genetic effects...

In this paper, we propose a semi-parametric model for autonomous nonlinear dynamical systems and devise an estimation procedure for model fitting. This model incorporates subjectspecific effects and can be viewed as a nonlinear semi-parametric mixed effects model. We also propose a computationally efficient model selection procedure. We prove consistency of the proposed estimator under suitable...

During financial crises equity portfolios have suffered large losses. Methodologies for portfolio selection taking into account the possibility of large losses have existed for decades but their economic value is not well established. This article investigates the economic value in reducing the probability of large losses in portfolio selection. We combine mean-variance analysis with semi-param...

Genest et al. (1995) proposed a two-stages semi-parametric estimation procedure for bivariate Archimedean copulas. A three stage semi-parametric estimation method based on Kendall’s tau has been recently proposed in the financial literature to estimate the Student’s T copula, too. Its major advantage is to allow for greater computational tractability when dealing with high dimensional issues, w...

This paper studies the identification and estimation of a basic model of technology adoption using specifically collected information on subjective beliefs and expectations to identify key model parameters. We discuss identification with both non-parametrically and parametrically specified utility as well as parametric and semi-parametric specifications for unobserved heterogeneity. We propose ...