We propose a non-linear density estimator, which is locally adaptive, like wavelet estimators, and positive everywhere, without a logor root-transform. This estimator is based on maximizing a non-parametric log-likelihood function regularized by a total variation penalty. The smoothness is driven by a single penalty parameter, and to avoid cross-validation, we derive an information criterion ba...

Without randomization of treatments, valid inference treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically control subjects. Confounding is commonly achieved using propensity score, defined as conditional probability assignment to a given observed covariates. The score collapses covariates into single m...

In the past decades growing amount of network data lead to many novel statistical models. this paper we consider so-called geometric networks. Typical examples are road networks or other infrastructure Nevertheless, neurons blood vessels in a human body can also be interpreted as embedded three-dimensional space. A network-specific metric, rather than Euclidean is usually used all these applica...

We propose a fast penalized spline method for bivariate smoothing. Univariate Pspline smoothers Eilers and Marx (1996) are applied simultaneously along both coordinates. The new smoother has a sandwich form which suggested the name “sandwich smoother” to a referee. The sandwich smoother has a tensor product structure that simplifies an asymptotic analysis and it can be fast computed. We derive ...

Modern techniques for fitting generalized additive models mostly rely on basis expansions of covariates using a large number of basis functions and penalized estimation of parameters. We exemplarily use a mixed model approach to fit a model for children’s lung function that allows for non-linear influence of several covariates available in a substantial data set. While the resulting model is ex...

Functional data analysis techniques, such as penalized splines, have become common tools used in a variety of applied research settings. Penalized spline estimators are frequently to estimate unknown functions from noisy data. The success these depends on choosing tuning parameter that provides the correct balance between fitting and smoothing Several different selection methods been proposed f...

Abstract. Semiparametric model is a statistical model consisting of both parametric and nonparametric components, which can be looked on as a mixture model. The theoretical properties of this model have been studied extensively, such as large-sample property. However, most researches are based on scalar value, in which the dimension of the observation is one at each moment. In the fields of spa...