Smoothed Conditional Scale Function Estimation in AR(1)-ARCH(1) Processes

Smoothed L-estimation of regression function

The Nadaraya-Watson estimator of regression is known to be highly sensitive to the presence of outliers in the sample. A possible way of robusti-fication consists in using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. We show that ...

Estimation of Scale Parameter Under a Bounded Loss Function

     The quadratic loss function has been used by decision-theoretic statisticians and economists for many years.  In this paper  the estimation of scale parameter under a bounded loss function, which is adequate for assessing quality and quality improvement, is considered with restriction to the principles of invariance and risk unbiasedness. An implicit form of minimum risk scale equivariant ...

ESTIMATION OF SCALE PARAMETER UNDER A REFLECTED GAMMA LOSS FUNCTION

In this paper, the estimation of a scale parameter t under a new and bounded loss function, based on a reflection of the gamma density function, is discussed. The best scale-invariant estimator of tis obtained and the admissibility of all linear functions of the sufficient statistic, for estimating t in the absence of a nuisance parameter, is investigated

Conditional transformation models for survivor function estimation.

In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient's risk than summary statistics, such as median survival time. Nevertheless, standard methods fo...

Adaptive pointwise estimation of conditional density function