Oracally Efficient Two-Step Estimation of Generalized Additive Model

Oracally efficient two-step estimation for additive regression

Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band

Under weak conditions of smoothness and mixing, we propose splinebackfitted spline (SBS) estimators of the component functions for nonlinear additive autoregression model. The proposed SBS estimator is both computationally expedient for analyzing high dimensional large time series data since it can circumvent the curse of dimensionality, and theoretically reliable as the estimator is oracally e...

Spline-backfitted kernel smoothing of partially linear additive model

A spline-backfitted kernel smoothing method is proposed for partially linear additive model. Under assumptions of stationarity and geometric mixing, the proposed function and parameter estimators are oracally efficient and fast to compute. Such superior properties are achieved by applying to the data spline smoothing and kernel smoothing consecutively. Simulation experiments with both moderate ...

Estimation in Simple Step-Stress Model for the Marshall-Olkin Generalized Exponential Distribution under Type-I Censoring

This paper considers the simple step-stress model from the Marshall-Olkin generalized exponential distribution when there is time constraint on the duration of the experiment. The maximum likelihood equations for estimating the parameters assuming a cumulative exposure model with lifetimes as the distributed Marshall Olkin generalized exponential are derived. The likelihood equations do not lea...

A case study on using generalized additive models to fit credit rating scores

We consider the estimation of credit scores by means of semiparametric logit models. In credit scoring, the fitted rating score shall not only provide an optimal classification result but serves also as a modular component of a (typically quite complex) rating system. This means in particular that a rating score should be given by a linearly weighted sum of rating factors. That way the rating p...