Classical two-sample permutation tests for equality of distributions have exact size in finite samples, but they fail to control testing parameters that summarize each distribution. This article proposes are estimated at root-n or slower rates. Our general framework applies both parametric and nonparametric models, with two samples one sample split into subsamples. correct asymptotically while ...

Semiparametric estimation methods are used for models which are partly parametric and partly nonparametric; typically the parametric part is an underlying regression function which is assumed to be linear in the observable explanatory variables, while the nonparametric component involves the distribution of the model's ‘error terms’. Semiparametric methods are particularly useful for limited de...

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first differenced model. One is composed of a semiparametric GMM esti...

In this paper we describe some general methods for constructing goodness of t tests in nonparametric regression models. Our main concern is the development of statisticial methodology for the assessment (validation) of speci c parametric modelsM as they arise in various elds of applications. The fundamental idea which underlies all these methods is the investigation of certain goodness of t sta...

During the past two decade, linkage analysis has been phenomenally successful in localizing Mendelian disease genes. Now, human genetics moves toward identification of genes contributing to the susceptibility to common and non-Mendelian diseases, such as hypertension, diabetes, heat disease, familial cancer, and so on. In parametric linkage analysis, mode of inheritance of the disease susceptib...