pgam.fit | R Documentation |
Estimate one-step ahead expectation and variance of y_{t} conditional on observed time series until the instant t-1.
pgam.fit(w, y, eta, partial.resid)
w |
estimate of discount factor ω of a Poisson-Gamma model |
y |
observed time series which is the response variable of the model |
eta |
semiparametric predictor |
partial.resid |
type of partial residuals. |
Partial residuals for semiparametric estimation is extracted. Those are regarded to the parametric partition fit of the model. Available types are raw
, pearson
and deviance
. The type raw
is prefered. Properties of other form of residuals not fully tested. Must be careful on choosing it.
See details in predict.pgam
and residuals.pgam
.
yhat |
vector of one-step ahead prediction |
resid |
vector partial residuals |
This function is not intended to be called directly.
Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br
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.
Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and Generalized Linear Models: a roughness penalty approach. Chapman and Hall, London
pgam
, residuals.pgam
, predict.pgam
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