pgam.likelihood: Likelihood function to be maximized

Description Usage Arguments Details Value Note Author(s) References See Also

View source: R/pgam.r

Description

This is the log-likelihood function that is passed to optim for likelihood maximization.

Usage

1
pgam.likelihood(par, y, x, offset, fperiod, env = parent.frame())

Arguments

par

vector of parameters to be optimized

y

observed time series which is the response variable of the model

x

observed explanatory variables for parametric fit

offset

model offset. Just like in GLM

fperiod

vector of seasonal factors to be passed to pgam.par2psi

env

the caller environment for log-likelihood value to be stored

Details

Log-likelihood function of hyperparameters ω and β is given by

\log L≤ft(ω,β\right)=∑_{t=τ+1}^{n}{\log Γ≤ft(a_{t|t-1}+y_{t}\right)-\log y_{t}!-\cr \log Γ≤ft(a_{t|t-1}\right)+a_{t|t-1}\log b_{t|t-1}-≤ft(a_{t|t-1}+y_{t}\right)\log ≤ft(1+b_{t|t-1}\right)}

where a_{t|t-1} and b_{t|t-1} are estimated as it is shown in pgam.filter.

Value

List containing log-likelihood value, optimum linear predictor and the gamma parameters vectors.

Note

This function is not intended to be called directly.

Author(s)

Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br

References

Harvey, A. C., Fernandes, C. (1989) Time series models for count data or qualitative observations. Journal of Business and Economic Statistics, 7(4):407–417

Harvey, A. C. (1990) Forecasting, structural time series models and the Kalman Filter. Cambridge, New York

Junger, W. L. (2004) Semiparametric Poisson-Gamma models: a roughness penalty approach. MSc Dissertation. Rio de Janeiro, PUC-Rio, Department of Electrical Engineering.

See Also

pgam, pgam.filter, pgam.fit


pgam documentation built on May 2, 2019, 10:42 a.m.

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