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SUMMARY We present a new method for data‐based selection of the bandwidth in kernel density estimation which has excellent properties. It improves on a recent procedure of Park and Marron (which itself is a good method) in various ways. First, the new method has superior theoretical performance; second, it also has a computational advantage; third, the new method has reliably good performance for smooth densities in simulations, performance that is second to none in the existing literature. These methods are based on choosing the bandwidth to (approximately) minimize good quality estimates of the mean integrated squared error. The key to the success of the current procedure is the reintroduction of a non‐stochastic term which was previously omitted together with use of the bandwidth to reduce bias in estimation without inflating variance.

A minimum divergence estimation method is developed for robust parameter estimation and model fitting. The proposed approach uses new density-based divergences which, unlike existing density-based minimum divergence methods (e.g. minimum Hellinger distance estimation), avoid the use of nonparametric density estimation and associated complications such as bandwidth selection. The proposed class of 'density power divergences' is indexed by a single parameter a which can be varied to study the trade-off between robustness and efficiency. The method can be viewed as a robust extension of maximum likelihood estimation, since the class of divergences contains the Kullback-Leibler divergence when a = 0. Choices of a near zero afford robustness while retaining efficiency close to that of maximum likelihood.

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If a probability density function has bounded support, kernel density estimates often overspill the boundaries and are consequently especially biased at and near these edges. In this paper, we consider the alleviation of this boundary problem. A simple unified framework is provided which covers a number of straightforward methods and allows for their comparison: 'generalized jackknifing' generates a variety of simple boundary kernel formulae. A well-known method of Rice (1984) is a special case. A popular linear correction method is another: it has close connections with the boundary properties of local linear fitting (Fan and Gijbels, 1992). Links with the 'optimal' boundary kernels of M011er (1991) are investigated. Novel boundary kernels involving kernel derivatives and generalized reflection arise too. In comparisons, various generalized jackknifing methods perform rather similarly, so this, together with its existing popularity, make linear correction as good a method as any. In an as yet unsuccessful attempt to improve on generalized jackknifing, a variety of alternative approaches is considered. A further contribution is to consider generalized jackknife boundary correction for density derivative estimation. En route to all this, a natural analogue of local polynomial regression for density estimation is defined and discussed.