View source: R/hermite_estimator_univar.R

hermite_estimator_univar | R Documentation |

This method constructs an S3 object with associated methods for univariate nonparametric estimation of pdfs, cdfs and quantiles.

```
hermite_estimator_univar(
N = 50,
standardize = TRUE,
exp_weight_lambda = NA,
observations = c()
)
```

`N` |
An integer between 0 and 75. The upper bound has been chosen as a value that yields an estimator that is reasonably fast and that remains robust to numerical issues. The Hermite series based estimator is truncated at N+1 terms. |

`standardize` |
A boolean value. Determines whether the observations are standardized, a transformation which often improves performance. |

`exp_weight_lambda` |
A numerical value between 0 and 1. This parameter controls the exponential weighting of the Hermite series based estimator. If this parameter is NA, no exponential weighting is applied. |

`observations` |
A numeric vector. A vector of observations to be incorporated into the estimator. |

The hermite_estimator_univar class allows the sequential or one-pass batch estimation of the full probability density function, cumulative distribution function and quantile function. It is well suited to streaming data (both stationary and non-stationary) and to efficient estimation in the context of massive or distributed data sets. Indeed, estimators constructed on different subsets of a distributed data set can be consistently merged.

An S3 object of class hermite_estimator_univar, with methods for density function, distribution function and quantile function estimation.

Michael Stephanou <michael.stephanou@gmail.com>

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