In this paper, we merge the parametric structure of neural networks into a segmental semi-Markov model to set up a Bayesian framework for protein structure prediction. The parametric model, which can also be regarded as an extension of a sigmoid belief network, captures the underlying dependency in residue sequences. The results of numerical experiments indicate the usefulness of this approach.

Bioequivalence assessment is an issue of great interest. Development of statistical methods for assessing bioequivalence is an important area of research for statisticians. Bioequivalence is usually determined based on the normal distribution. We relax this assumption and develop a semi-parametric mixed model for bioequivalence data. The proposed method is quite flexible and practically meaning...

the purpose of this study is estimation of daily value at risk (var) for total index of tehran stock exchange using parametric, nonparametric and semi-parametric approaches. conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated var and also to compare the performance of mentioned approaches. in most cases, based on backtesting statistics results, ...

Self-modeling regression is a powerful semi-parametric tool for analysis of longitudinal data with time-invariant covariates. This paper explores the use of self-modelling regression for tting data from a designed experiment in which the response is a curve. Recent advances in mixed nonlinear models and nonparametric regression with time series errors has made the use of mixed model self-modeli...

One commonly encountered problem in statistical analysis of extreme events is that very few data are available for inference. This issue is all the more important in multivariate problems that the dependence structure among extremes has to be inferred. In some cases, e.g. in environmental applications, it is sometimes possible to increase the sample size by taking into account historical or inc...

This article proposes a semi-parametric stochastic frontier model (SPSF) in which components of the technology and of technical efficiency are represented using semi-parametric methods and estimated in a Bayesian framework. The approach is illustrated in an application to US farm data. The analysis shows important scale economies for small and medium herds and constant return to scale for large...