The estimation and inference for conditional estimating equations models with endogenous selection, are considered. approach takes into account possible selection which may lead to a bias. It can be used wide range of statistical not covered by the model-based sampling theory. Endogeneity either part or within covariates. is particularly well suited unknown heteroscedasticity, uncontrolled conf...

The data envelopment analysis is related to a non-parametric mathematical tool used assess the relative efficiency of productive units. In different studies on efficiency, it common employ semi-parametric procedures in two stages determine whether any exogenous factors interest affect performance However, some these procedures, particularly those based conventional statistical inference, genera...

This paper undertakes a general reliability study using both classical and Bayesian semi-parametric degradation approaches. The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework. With the proposed classical approach, both accelerated life tests (ALT...

Consider the semi-parametric linear regression model Y = ′X+ , where has an unknown distribution F0. The semi-parametric MLE
̃ of under this set-up is called the generalized semi-parametric MLE(GSMLE).Although the GSML estimation of the linear regression model is statistically appealing, it has never been attempted due to difficulties with obtaining the GSML estimates of and F until recent work...

A Bayesian semi-parametric estimation of the binary response model using Markov Chain Monte Carlo algorithms is proposed. The performances of the parametric and semi-parametric models are presented. The mean squared errors, receiver operating characteristic curve, and the marginal effect are used as the model selection criteria. Simulated data and Monte Carlo experiments show that unless the bi...

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in largescale video annotation due to several difficulties, such as large variation of video conte...

This paper models zero response data from willingness to pay surveys by employing parametric and semi-parametric estimation methods. The result of the specification test indicates the semi-parametric estimation outperforms the parametric estimation significantly.  2001 Published by Elsevier Science B.V.

The choice of a parametric model structure in empirical and semi-empirical non-linear modeling is usually viewed as an important and critical step. However, it is known that by augmenting the least-squares identiication criterion with a term that imposes penalty on the non-smoothness of the model, an optimal non-parametric model can be found explicitly. The optimal non-parametric model will dep...