We consider a large class of transformation models introduced by Gu et al. (2005)  [14]. They proposed an estimation procedure for calculating the maximum partial marginal likelihood estimator (MPMLE) of regression parameters. A big advantage of MPMLE is that it avoids estimating two...

In view of the cumbersome and often intractable numerical integrations required for a full likelihood analysis, several suggestions have been made recently for approximate inference in generalized linear mixed models (GLMMs). Two closely related approximate methods are the penalized...

Chen et al. (2010) [1] propose a unified method–coordinate-independent sparse estimation (CISE)–that is able to simultaneously achieve sparse sufficient dimension reduction and screen out irrelevant and redundant variables efficiently. However, its attractive features depend on the...

We propose a criterion for variable selection in discriminant analysis. This criterion permits to arrange the variables in decreasing order of adequacy for discrimination, so that the variable selection problem reduces to that of the estimation of suitable permutation and dimensionality. Then,...

In this paper, we are concerned with the estimating problem of functional coefficient regression models with generated covariates. A new local polynomial estimation is proposed, which is based on error covariance matrix correction. It is shown that the resulting estimators are consistent,...