Recently, several authors have introduced and discussed the advantage of the use of item response theory models (IRT) to construct personality scales and to explore the structure of personality data sets. For example, Waller, Tellegen, McDonald, and Lykken (1996) contrasted the use of IRT to principal factor analysis and Reise and Waller (in press) discussed the choice of an IRT model to analyz...

An overview is presented of some parametric and semi-parametric models, estimators, and specification tests that can be used to analyze ordered response variables. In particular, limited dependent variable models that generalize ordered probit are compared to regression models that generalize the linear model. These techniques are then applied to analyze how self-reported satisfaction with hous...

We explore the in- and out-of-sample robustness of tests for choice inconsistencies based on parameter restrictions in parametric models, focusing on tests proposed by Ketcham, Kuminoff, and Powers (2016). We argue that their nonparametric alternatives are inherently conservative with respect to detecting mistakes. We then show that our parametric model is robust to KKP's suggested specificatio...

We suggest a method for using nonparametric information to modify a parametric model at a low-order level, retaining information in the model only to enhance the nonparametric approach at relatively high orders. Our technique represents an alternative to methods that rst t a parametric model and then adjust it. In particular, relative to a \nonparametric estimator with a parametric start," our ...

In the context of testing the speci cation of a nonlinear parametric regression function, we study the power of speci cation tests using the minimax approach. We determine the maximum rate at which a set of smooth local alternatives can approach the parametric model while ensuring consistency of a test uniformly against any alternative in this set. We show that a smooth nonparametric testing pr...

This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoretical basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduction to missing data within regression analysis an...

This article proposes a new general methodology for constructing nonparametric asymptotic distribution-free tests for semiparametric hypotheses in regression models. Tests are based on the difference between the estimated restricted and unrestricted regression errors’ distributions. A suitable integral transformation of this difference renders the tests asymptotically distribution-free, with li...