We describe some recent development of nonparametric methods for estimating conditional quantile functions using additive models with total variation roughness penalties. We focus attention primarily on selection of smoothing parameters and on the con

Conditional copulas are flexible statistical tools that couple joint conditional and marginal conditional distributions. In a linear regression setting with more than one covariate and two dependent outcomes, we consider additive models for studying the dependence between covariates and the copula parameter. We examine the computation and model selection tools needed for Bayesian inference. The...

In this paper we consider additive models in censored regression. We propose a randomly weighted version of the backfitting algorithm that allows for the nonparametric estimation of the effects of the covariates on the response. Given the high computational cost involved, binning techniques are used to speed up the computation in the estimation and testing process. Simulation results and the ap...

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for...

13 Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used 14 approach to study protein-DNA interactions. To analyze ChIP-Seq data, practitioners are 15 required to combine tools based on different statistical assumptions and dedicated to spe16 cific applications such as calling protein occupancy peaks or testing for differential occu17 pancies. Here, we present Ge...

This paper presents new instrumental variables estimators for nonparametric models with discrete endogenous regressors. The model speci cation is su ciently general to include structural models, triangular simultaneous equations and certain models of measurement error. Restricting the analysis to discrete endogenous regressors is an integral component of the analysis since a similar model with ...

We study smooth backfitting when there are errors-in-variables, which is motivated by functional additive models for a functional regression model with a scalar response and multiple functional predictors that are additive in the functional principal components of the predictor processes. The development of a new smooth backfitting technique for the estimation of the additive component function...

In this paper we model speckle as signal-dependent additive noise, and propose to use the normal inverse Gaussian distribution as a statistical model for the wavelet coefficients of both the reflectance image and noise image. Further, we show how to estimate the parameters specifying these distributions from the parameters included in the K-distribution modeling the intensity of a SAR image. we...

data envelopment analysis (dea) models with interval inputs and outputs have been rarely discussed in dea literature. this paper, using the enhanced russell measurement proposes an extended model which permits the presence of interval scale variables which can take both negative and positive values. the model is compared with most well-known dea models of which include the ccr model, the bcc mo...