This paper presents a new method for spatially adaptive local likelihood estimation which applies to a broad class of nonparametric models, including the Gaussian, Poisson and binary response models. The main idea of themethod is given a sequence of local likelihood estimates (``weak´´...

To estimate cell probabilities for ordered sparse contingency tables several smooth- ing techniques have been investigated. It has been recognized that nonparametric smoothing methods provide estimators of cell probabilities that have better performance than the pure frequency estimators. With...

The choice of a smoothing parameter or bandwidth is crucial when applying non- parametric regression estimators. In nonparametric mean regression various meth- ods for bandwidth selection exists. But in nonparametric quantile regression band- width choice is still an unsolved problem. In this...

This note uses the established connection between the local-EM and EMS algorithms to explore methods for smoothing mismeasured data. Implementations of local-EM are developed for several forms of censored and/or mismeasured data and their connections with other methods explored.

This paper develops models aimed at more accurate estimation of the medical cost function based on the individual cost data. In our proposed models, the cost data are assumed to be dependent on the whole clinical evolution via Markov transition probabilities, and the accumulative rate of cost in...

We are interested in modeling the mortality of long-term care (LTC) claimants having the same level of severeness (heavy claimant). Practitioners often use empirical methods that rely heavily on expert opinions. We propose approaches not depending on an expert’s advice. We analyze the...

It is a common view among finance analysts and econometricians that the correlation between financial objects becomes stronger as the market is going down, and that it approaches one when the market crashes, having the effect of destroying the benefit of diversification. The purpose of this...

A local likelihood density estimator is shown to have asymptotic bias depending on the dimension of the local parameterization. Comparing with kernel estimation it is demonstrated using a variety of bandwidths that we may obtain as good and potentially even better estimates using local...