A Smoothness Priors–State Space Modeling of Time Series with Trend and Seasonality

Kitagawa, Genshiro; Gersch, Will

doi:10.1080/01621459.1984.10478060

Cited by 127 publications

(97 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The appropriateness of this model, even when dealing with short series, is one of its advantages. 7 Our results indicate substantial underregistration of cholera mortality during the epidemic in the Northeast Region. Assuming that the excess mortality between 1992 and 1994 was indeed caused by cholera, this would mean that only 19.3% of cholera cases in the period were actually reported.…”

Section: Discussionmentioning

confidence: 68%

See 1 more Smart Citation

Sobremortalidade por diarréia simultânea à cólera na região Nordeste do Brasil

Gerolomo

Penna

2004

Rev. Saúde Pública

View full text Add to dashboard Cite

ObjectiveTo evaluate excess mortality due to infectious diarrhea without etiological diagnosis, occurring simultaneously to Vibrio cholerae circulation. Methods Statistical modeling was applied to a time series of deaths ascribed to "poorly defined intestinal infections" (International Classification of Diseases -ICD-9) and "presumably infectious diarrhea and gastroenteritis" (ICD-10), between 1980 and 1998 in the Northeast Region of Brazil. The prediction of expected values was obtained after a discontinuity point. Excess mortality was calculated as the difference between observed figures and those estimated by the model. Results

show abstract

Section: Discussionmentioning

confidence: 68%

“…The AIC minimum value was used for selecting the best model. 7 Model parameters were estimated using Kyplot software. The adjusted model was used for predicting expected values after the point of discontinuity.…”

Section: Introductionmentioning

confidence: 99%

Sobremortalidade por diarréia simultânea à cólera na região Nordeste do Brasil

Gerolomo

Penna

2004

Rev. Saúde Pública

View full text Add to dashboard Cite

show abstract

“…= {/(()),...,/(«)} e R" +1 , s = [s (-3),..., s(n)}' € K n+4 , and f 2 e ft. Although we can minimize (3.1) by applying the backfitting algorithm to the individual components t, s, and / 2 , it is computationally more efficient to form the composite component/i(/) = {t(i), s(i)}, define the functional y,i(/i) =t(i)+s(i) and Y i2 {fi) = fiixn), i = 1, ...,n and apply the backfitting algorithm to the two component model y(i) = Yu(fi) + hixn) + e(0-Thus t and s are estimated simultaneously as in Kitagawa and Gersch (1984). We note that the functionals y n are not evaluation functionals.…”

Section: Degenerate Casementioning

confidence: 99%

Convergence of the backfitting algorithm for additive models

Ansley

Kohn²

1994

J Aust Math Soc A

View full text Add to dashboard Cite

The backfitting algorithm is an iterative procedure for fitting additive models in which, at each step, one component is estimated keeping the other components fixed, the algorithm proceeding component by component and iterating until convergence. Convergence of the algorithm has been studied by Buja, Hastie, and Tibshirani (1989). We give a simple, but more general, geometric proof of the convergence of the backfitting algorithm when the additive components are estimated by penalized least squares. Our treatment covers spline smoothers and structural time series models, and we give a full discussion of the degenerate case. Our proof is based on Halperin's (1962) generalization of von Neumann's alternating projection theorem.1991 Mathematics subject classification (Amer. Math. Soc): 62G05, 62J02, 65DO7, 65D10.

show abstract

“…Many authors have investigated the problem of non-linear and Gaussian model. These includes the studied of the effect of non-Gaussian on state space [1][2][3][4][5][7][8][9][10][11][12][13][14]16] . Effort were made to considered using modified quadratic hill climbing method to adjust for quadratic hill climbing method originally developed by Goldfield, Quandt and Trotter [6] , in solving nonlinear and non-Gaussian contaminated with nonnegative definite second derivative.…”

Section: Introductionmentioning

confidence: 99%

Hill Climbing Method Using Claus Model for Categorical Data

Onoghojobi¹

2009

J. of Mathematics and Statistics

View full text Add to dashboard Cite

Problem statement: In this study, a locally unbiased test statistic based on the Claussian method was presented. Approach: In this respect, exploiting special structures of modified quadratic hill climbing method and the Claussian statistics were considered. Results: We derived a non-modified quadratic hill-climbing procedure. The performance of the model in testing the estimation of Latent data was shown. Conclusion/Recommendations: The result testing for serial Kalman suggested that, computational simplicity may outweigh any non-negative definite in terms of second derivative matrix.

show abstract

A Smoothness Priors–State Space Modeling of Time Series with Trend and Seasonality

Cited by 127 publications

References 18 publications

Sobremortalidade por diarréia simultânea à cólera na região Nordeste do Brasil

Sobremortalidade por diarréia simultânea à cólera na região Nordeste do Brasil

Convergence of the backfitting algorithm for additive models

Hill Climbing Method Using Claus Model for Categorical Data

Contact Info

Product

Resources

About