restriction, the semiparametric information bound is zero, even when the homoskedastic error term is parametrically specified …

Semi-parametric and nonparametric modeling and inference have been widely studied duringthe last two decades. In this ….Firstly, we develop a semi-parametric additivity test for nonparametric multi-dimensionalmodel. The test statistic can test two or …

simulation, and extreme value theory. A new semiparametric model based on density ratio is developed in Chapter three. By … the VaR. We ran a monte carlo simulation to compare the semiparametric model and the traditional VaR models under many … different scenarios. In several cases, the semiparametric model performs quite satisfactorily. Furthermore, when applied to real …

A multiple-sample semiparametric density ratio model, which is equivalent to a generalized logistic regression model … nonparametric.The combined data from all the samples are used in the semiparametric large sample problem of estimating each … class of semiparametric models to time series. The method combines information from several time series and estimates their …

approach.In order to estimate the distribution of random effects and to improve efficiency, we further propose a semiparametric … inferences under departures from this assumption and are competitive when the assumption holds. The semiparametric full …

semiparametric varying coefficient periodic model which is suitable to studyperiodic patterns. This model has ample applications in …