We develop a new method for assessing the adequacy of a smooth regression function based on nonparametric regression and the bootstrap. Our methodology allows users to detect systematic misfit and to test hypotheses of the form ‘‘the proposed smooth regression model is not significantly different from the smooth regression model that generated these data.’’ We also provide confidence bands on t...

This article deals with the analysis of a hierarchical semiparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N3), where N is the total number of observations in the sample. Therefore, nonparametric est...

In this paper we derive nonparametric stochastic volatility models in discrete time. These models generalize parametric autoregressive random variance models, which have been applied quite successfully to nancial time series. For the proposed models we investigate nonparametric kernel smoothers. It is seen that so-called nonparametric deconvolution estimators could be applied in this situation ...

The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused...

Count data arises for example in bioinformatics or analysis of text documents represented as word count vectors. With several data sets available from related sources, exploiting their similarities by transfer learning can improve models compared to modeling sources independently. We introduce a Bayesian generative transfer learning model which represents similarity across document collections ...

Following a framework proposed in Bickel, Ritov and Stoker (2001) we propose and analyze the behavior of a broad family of tests for H : E(Y | U, V ) = E(Y | U) when we observe (Ui, Vi, Yi) ∈ Ru i.i.d., i = 1, . . . , n.

Nonparametric hypothesis testing procedures based on the bootstrap were developed in testing for constant clustering effect in a survival model that incorporates the clustering effect into the Cox Proportional Hazards model. In a clustered survival model, bootstrap estimators of the cluster-specific parameters are consistent. Simulation studies indicate that the procedure is correctly-sized and...

Generalized linear models and quasi-likelihood method extend the ordinary regression models to accommodate more general conditional distributions of the response. Nonparametric methods need no explicit parametric specification and the resulting model is completely determined by the data themselves. However nonparametric estimation schemes generally have a slower convergence rate such as the loc...

Data Envelopment Analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper we show that DEA can be alternatively interpreted as nonparametric least squares regression subject to shape constraints on frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and C...