Get COMPSTAT: Proceedings in Computational Statistics 12th PDF

By George Box (auth.), Professor Dr. Albert Prat (eds.)

ISBN-10: 3642469922

ISBN-13: 9783642469923

ISBN-10: 3790809535

ISBN-13: 9783790809534

COMPSTAT symposia were held on a regular basis on account that 1974 once they begun in Vienna. this custom has made COMPSTAT an important discussion board for the interaction of data and machine sciences with contributions from many renowned scientists around the world. The medical programme of COMPSTAT '96 covers all points of this interaction, from user-experiences and overview of software program in the course of the improvement and implementation of recent statistical rules. All papers awarded belong to 1 of the 3 following different types: - Statistical equipment (preferable new ones) that require a considerable use of computing; - machine environments, instruments and software program worthwhile in records; - purposes of computational information in parts of considerable curiosity (environment, health and wellbeing, undefined, biometrics, etc.).

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Bruce, A. G. Martin, R. D. and Yohai, V. (1992). Two new robust methods for time series, COMPSTATI0, 321-326. , Tiao, G. , and Chen, C. (1988). Estimation of time series parameters in the presence of outliers. Technometrics, 30, 193-204. Harvey, A. C. and Phillips, C. D. A. (1976). The maximum likelihood estimation of ARMA models by Kalman filtering. 3, University of Canterbury. 38 Harvey, A. C. and Phillips, C. D. A. (1979). Maximum likelihood estimation regresssion models with autoregressive-moving average disturbances.

Pesaran, M. H. (1973). Exact maximum likelihood estimation of a regresssion equation with a first order moving average. Rev. Econ. Studies, 40, 529-535. Pierce, D. A. (1971). Least squares estimation in the regresssion model with autoregresssive-moving average errors. Biometrika, 58, 299-312. Tsay, R. S. (1988). Outliers, level shifts and variance changes in time series. J. Forecasting, 7, 1-20. Rousseeuw P. J. and Yohai, V. J. (1984). Robust regression by means ofSestimators, in Robust and Nonlinear Time Series Analysis, J.

P. (1987). An iterative GLS approach to maximum likelihood estimation of regression models with ARIMA errors. Research Report No. C. Otto, M. C. and Bell, W. R. (1990). Two issues in time series outlier detection using indicator variables. Proceedings of the American Statistical Association, Business and Economics Statistics Section, 170-174. Pagan, A. R. and Nicholls, D. F. (1976). Exact maximum likelihood estimation of regression models with finite order moving average errors. Rev. Econ. Studies, 43, 383-387.

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COMPSTAT: Proceedings in Computational Statistics 12th Symposium held in Barcelona, Spain, 1996 by George Box (auth.), Professor Dr. Albert Prat (eds.)


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