In this paper, we show that the hinge loss can be interpreted as the neglog-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semiparametric model fitted by a maximum a posteriori estimation procedure. This connection enables to derive a mapping from SVM scores to estimated posterior probabilities. Unlike prev...

We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mi...

We propose a new linear method for dimension reduction to identify nonGaussian components in high dimensional data. Our method, NGCA (non-Gaussian component analysis), uses a very general semi-parametric framework. In contrast to existing projection methods we define what is uninteresting (Gaussian): by projecting out uninterestingness, we can estimate the relevant non-Gaussian subspace. We sho...

In this paper, we proposed a semi-parametric single-index two-part regression model to weaken assumptions in parametric regression methods that were frequently used in the analysis of skewed data with additional zero values. The estimation procedure for the parameters of interest in the model was easily implemented. The proposed estimators were shown to be consistent and asymptotically normal. ...

This article proposes a new class of rating scale models, which merges advantages and overcomes shortcomings of the traditional linear and ordered latent regression models. Both parametric and semi-parametric estimation is considered. The insights of an empirical application to satisfaction data are threefold. First, the methods are easily implementable in standard statistical software. Second,...

Many currently popular models of categorization are either strictly parametric (e.g., prototype models, decision bound models) or strictly nonparametric (e.g., exemplar models) (Ashby & Alfonso-Reese, 1995). In this article, a family of semi-parametric classifiers is investigated where categories are represented by a finite mixture distribution. The advantage of these mixture models of categori...

The objective of hazards (lifetime) analysis is to advance and promote statistical science in the various applied fields that deal with lifetime (survival) data including: actuarial science and reliability engineering. The lifetime data analysis provides special techniques that are required to compare the risks for failure. Bayesian semi-parametric methods have been applied to survival analysis...