<b>lpdensity</b>: Local Polynomial Density Estimation and Inference

Local Polynomial Variance Function Estimation

The conditional variance function in a heteroscedastic, nonparametric regression model is estimated by linear smoothing of squared residuals. Attention is focussed on local polynomial smoothers. Both the mean and variance functions are assumed to be smooth, but neither is assumed to be in a parametric family. The eeect of preliminary estimation of the mean is studied, and a \degrees of freedom"...

Bayesian Density Estimation and Inference Using Mixtures

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Bayesian Density Estimation and Inference Using

We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation, and are exempliied by special cases where data are modelled as a sample from mixtures of normal distributions. EEcient simulation methods are used to approximate various prior, posterior and predictive distributions. ...

Inference and Density Estimation with Interval Statistics

Individual data from a continuous distribution are often partitioned into a collection of intervals defined by either fixed interval limits or sample quantiles. In this study, we derive asymptotic distribution of interval statistics for both cases, allowing multiple sample statistics for each interval. Under fixed intervals, the covariance matrix is singular. We identify a computationally simpl...

Local Maximum Likelihood Estimation and Inference