<p>
The data consist of
monthly counts of poliomyelitis cases in the USA from the year 1970 to 1983.</p>

datasets

Due to a large quantity of non-Gaussian time series and reliability data, the R-package non-Gaussian state-space with exact marginal likelihood is useful for modeling and forecasting non-Gaussian time series and reliability data via non-Gaussian state-space models with the exact marginal likelihood easily, see Gamerman, Santos and Franco (2013) <doi:10.1111/jtsa.12039> and Santos, Gamerman and Franco (2017) <doi:10.1109/TR.2017.2670142>. The package gives codes for formulating and specifying the non-Gaussian state-space models in the R language. Inferences for the parameters of the model can be made under the classical and Bayesian. Furthermore, prediction, filtering, and smoothing procedures can be used to perform inferences for the latent parameters. Applications include, e.g., count, volatility, piecewise exponential, and software reliability data.

T. dos Santos

NGSSEML

Non-Gaussian State-Space with Exact Marginal Likelihood

Yt function

<p>A data frame with 168 observations on the following 8 variables.</p>

Yt: The Polio Data

Description

Usage

Arguments

Format

Details

References

Examples