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References

1983; Wiley; Linguagem: Inglês

10.1002/9780470316610.refs

1940-6347

Bovas Abraham, Johannes Ledolter,

Advanced Statistical Methods and Models

Free Access References Bovas Abraham, Search for more papers by this authorJohannes Ledolter, Search for more papers by this author Book Author(s):Bovas Abraham, Search for more papers by this authorJohannes Ledolter, Search for more papers by this author First published: 19 September 1983 https://doi.org/10.1002/9780470316610.refsBook Series:Wiley Series in Probability and Statistics AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat References Abraham, B. (1980). Intervention analysis and multiple time series, Biometrika, 67, 73– 80. CrossrefWeb of Science®Google Scholar Abraham, B. (1983). Intervention model analysis, Encyclopedia of Statistical Sciences, Wiley, New York, (forthcoming). Google Scholar Abraham, B., and G. E. P. Box (1978). Deterministic and forecast adaptive time dependent models, Appl. Statist., 27, 120– 130. Wiley Online LibraryGoogle Scholar Abraham, B. (1979). Bayesian analysis of some outlier problems in time series, Biometrika, 66. 229– 236. CrossrefWeb of Science®Google Scholar Abraham, B., and C. Chatterjee (1982). Seasonal adjustment with X-11 ARIMA and forecast efficiency, Tech. Rep. STAT-82–05, Department of Statistics, University of Waterloo. Waterloo, Ontario. Google Scholar Ansley, C. F. (1979). An algorithm for the exact likelihood of a mixed autoregressive moving average process 1, Biometrika, 66, 59– 65. CrossrefWeb of Science®Google Scholar Ansley, C. F., and P. Newbold (1979). On the finite sample distribution of residual autocorrelations in autoregressive moving average models, Biometrika, 66, 547– 553. CrossrefWeb of Science®Google Scholar Ansley, C. F., and P. Newbold (1980). Finite sample properties of estimates for autoregressive moving average models. J. Econometrics, 13, 159– 183. CrossrefWeb of Science®Google Scholar Bartlett, M. S. (1946). On the theoretical specification of sampling properties of autocorrelated time series, J. Roy. Statist. Soc., Ser. B, 8, 27– 41. Wiley Online LibraryWeb of Science®Google Scholar Bartlett, M. S. (1955). Stochastic Processes, Cambridge University Press, Cambridge. Google Scholar Bass, F. M., and D. G. Clarke (1972). Testing distributed lag models of advertising effect, J. Market. Res., 9, 298– 308. CrossrefWeb of Science®Google Scholar Bhattacharyya, M. N., and A. P. Layton (1979). Effectiveness of seat belt legislation in the Queensland road toll—an Australian case study in intervention analysis, J. Amer. Statist. Assoc., 74, 596– 603. CrossrefWeb of Science®Google Scholar Blattberg, R. C., and A. P. Jeuland (1981). A micromodeling approach to investigate the advertising-sales relationship, Manage. Sci., 27, 988– 1005. CrossrefWeb of Science®Google Scholar Bongard, J. (1960). Some remarks on moving averages, Seasonal Adjustment on Electronic. Computers, OECD, Paris, pp. 361– 390. Google Scholar Bowerman, B. L., and R. T. O'Connell (1979). Time Series and Forecasting: An Applied Approach, Duxbury Press, North Scituate, MA. Google Scholar Box, G. E. P., and D. R. Cox (1964). An analysis of transformations, J. Roy. Statist. Soc., Ser. B, 26, 211– 243; discussion, 244–252. Wiley Online LibraryPubMedGoogle Scholar Box, G. E. P., S. C., Hillmer, and G. C. Tiao (1978). Analysis and modeling of seasonal time series, in Seasonal Analysis o/ Economic Time Series ( A. Zellner, Ed.), US. Department of Commerce, Bureau of the Census, Washington, DC, pp. 309– 333. Google Scholar Box, G. E. P., and G. M. Jenkins (1976). Time Series Analysis: Forecasting and Control, 2nd ed., Holden-Day, San Francisco. Google Scholar Box, G. E. P., and P. Newbold (1971). Some comments on a paper of Coen, Gomme and Kendall, J. Roy. Statist. Soc., Ser. A, 134, 229– 240. Wiley Online LibraryWeb of Science®Google Scholar Box, G. E. P., and D. A. Pierce (1970). Distribution of residual autocorrelations in autoregressive moving average time series models, J. Amer. Statist. Assoc., 65, 1509– 1526. Google Scholar Box, G. E. P., and G. C. Tiao (1975). Intervention analysis with applications to economic and environmental applications, J. Amer. Statist. Assoc., 70, 70– 79. Web of Science®Google Scholar Box, G. E. P., and G. C. Tiao (1976). Comparisons of forecasts and actuality, Appl. Statist., 25, 195– 200. Wiley Online LibraryWeb of Science®Google Scholar Brown, R. G. (1962). Smoothing, Forecasting and Prediction of Discrete Time Series, Prentice-Hall, Englewood Cliffs, NJ. Google Scholar Brown, R. G., and R. F. Meyer (1961). The fundamental theorem of exponential smoothing, Oper. Res., 9, 673– 685. CrossrefWeb of Science®Google Scholar Burman, J. P. (1965). Moving seasonal adjustment of economic time series, J. Roy. Statist. Soc., Ser. A, 128, 534– 538. Wiley Online LibraryWeb of Science®Google Scholar Burman, J. P. (1967). Assessment of a seasonal adjustment procedure by spectral analysis, Statistician, 17, 247– 256. Wiley Online LibraryWeb of Science®Google Scholar Burman, J. P. (1979). Seasonal adjustment—a survey, in TIMS Studies in the Management Sciences; Vol. 12, Forecasting ( S. Makridakis and S. C. Wheelwright, Eds.), North-Holland, Amsterdam, pp. 45– 57. Google Scholar Clarke, D. G. (1976). Econometric measurement of the duration of advertising effect on sales, J. Market. Res., 13, 345– 357. CrossrefWeb of Science®Google Scholar Cleveland, W. P. (1972). Analysis and forecasting of seasonal time series, unpublished Ph.D. dissertation, University of Wisconsin, Madison. Google Scholar Cleveland, W. P., and G. C. Tiao (1976). Decomposition of seasonal time series: a model for the Census X-11 program, J. Amer. Statist. Assoc., 71, 581– 587. Web of Science®Google Scholar Cleveland, W. S., D. M., Dunn, and I. J. Terpenning (1979). SABL—a resistant seasonal adjustment procedure with graphical methods for interpretation and diagnosis, in Seasonal Analysis of Economic Time Series ( A. Zellner, Ed.), U. S. Department of Commerce Bureau of the Census, Washington, DC, pp. 201– 231. Google Scholar Cogger, K. O. (1974). The optimality of general-order exponential smoothing, Oper. Res., 22, 858– 867. CrossrefWeb of Science®Google Scholar Computer programs for regression and time series analysis: BMDP: Biomedical Computer Programs, Series P, 1981 edition. Department of Bio-mathematics, University of California, Los Angeles. Google Scholar GLIM: Royal Statistical Society Working Party on Statistical Computing, London IDA: Interactive Data Analysis, SPSS Inc., Chicago, IL. Google Scholar IMSL: International Mathematical and Statistical Libraries, Houston, TX. Google Scholar MINITAB: Interactive (and Batch) Statistical Computing. Department of Statistics, Pennsylvania State University, University Park, PA. Google Scholar PACK-SYSTEM: Time Series Programs, developed by D. J. Pack; available from Automatic Forecast Systems, Inc., Hartboro, PA. Google Scholar SAS: Statistical Analysis System. SAS Institute Inc., Raleigh, NC. Google Scholar SPSS: Statistical Package for the Social Sciences. SPSS Inc., Chicago, IL. Google Scholar TS-Package: Time Series Programs, developed by A. I. McLeod; Department of Statistics, University of Waterloo, Ontario, Canada. Google Scholar WMTS-Package: The Wisconsin Multiple Time Series Program, developed by G. C. Tiao; Department of Statistics, University of Wisconsin, Madison, WI. Google Scholar Conley, D. L., G. S., Krahenbuhl, L. N. Burkett, and A. L. Millar (1981). Physiological correlates of female road racing performance, Res. Quart. Exercise Sport, 52, 441– 448. CrossrefCASPubMedWeb of Science®Google Scholar Dagum, E. B. (1975). Seasonal factor forecasts from ARIMA models, paper presented at the 40th session of the International Statistical Institute, Warsaw, Poland. Google Scholar Davies, N., C. M., Triggs, and P. Newbold (1977). Significance levels of the Box-Pierce portmanteau statistic in finite samples, Biometrika, 64, 517– 522. CrossrefWeb of Science®Google Scholar Dent, W. (1977). Computation of the exact likelihood function of an ARlMA process, J. Statist. Comput. Simulation, 5, 193– 206. CrossrefGoogle Scholar Dobbie, J. M. (1963). Forecasting periodic trends by exponential smoothing, Oper. Res., 11, 908– 918. CrossrefWeb of Science®Google Scholar Draper, N., and H. Smith (1981). Applied Regression Analysis, 2nd ed., Wiley, New York. Google Scholar Duncan, D. B., and S. D. Horn (1972). Linear dynamic regression estimation from the viewpoint of regression analysis, J. Amer. Statist. Assoc., 67, 815– 821. CrossrefWeb of Science®Google Scholar Durbin, J. (1960). The fitting of time series models, Rev. Int. Statist. Inst., 28, 233– 244. CrossrefGoogle Scholar Durbin, I., and G. S. Watson (1950). Testing for serial correlation in least squares regression I, Biometrika, 37, 409– 428. CrossrefCASPubMedWeb of Science®Google Scholar Durbin, I., and G. S. Watson (1951). Testing for serial correlation in least squares regression II, Biometrika, 38, 159– 178. CrossrefCASPubMedWeb of Science®Google Scholar Durbin, I., and G. S. Watson (1971). Testing for serial correlation in least squares regression III, Biometrika, 58, 1– 19. Web of Science®Google Scholar Erickson, G. M. (1981). Using ridge regression to estimate directly lagged effects in marketing, J. Amer. Statist. Assoc., 76, 766– 773. CrossrefWeb of Science®Google Scholar Fox, A. J. (1972). Outliers in time series, J. Roy. Statist. Soc., Ser. B, 34, 350– 363. Wiley Online LibraryWeb of Science®Google Scholar Garbade, K. (1977). Two methods for examining the stability of regression coefficients, J. Amer. Statist. Assoc., 72, 54– 63. Web of Science®Google Scholar Gilchrist, W. (1976). Statistical Forecasting, Wiley, New York. Google Scholar Godolphin, E. J., and P. J. Harrison (1975). Equivalence theorems for polynomial-projecting predictors, J. Roy. Statist. Soc., Ser. B, 37, 205– 215. Wiley Online LibraryWeb of Science®Google Scholar Granger, C. W. J., and P. Newbold (1974). Spurious regressions in econometrics, J. Econometrics, 2, 111– 120. CrossrefGoogle Scholar Granger, C. W. J., and P. Newbold (1976). Forecasting transformed series, J. Roy. Statist. Soc., Ser. B, 38, 189– 203. Wiley Online LibraryWeb of Science®Google Scholar Granger, C. W. J., and P. Newbold (1977). Forecasting Economic Time Series, Academic Press, New York. Google Scholar Harrison, P. J., and C. F. Stevens (1976). Bayesian forecasting, J. Roy. Statist. Soc., Ser. B, 38, 205– 228. Wiley Online LibraryWeb of Science®Google Scholar Helmer, R. M., and J. K. Johansson (1977). An exposition of the Box-Jenkins transfer function analysis with an application to the advertising-sales relationship, J. Market. Res., 14. 227– 239. CrossrefWeb of Science®Google Scholar Henderson, H. V., and P. F. Velleman (1981). Building multiple regression models interactively, Biometrics, 37, 391– 411. CrossrefWeb of Science®Google Scholar Hillmer, S. C., W. R., Bell, and G. C. Tiao (1982). Modeling considerations in the seasonal adjustment of economic time series, Tech, Rep. No. 665, Department of Statistics, University of Wisconsin, Madison. Google Scholar Hillmer, S. C., and G. C. Tiao (1979). Likelihood function of stationary multiple autoregressive moving average models, J. Amer. Statist. Assoc., 74, 652– 660. Web of Science®Google Scholar Hillmer, (1982). An ARIMA-model-based approach to seasonal adjustment, J. Amer. Statist. Assoc., 77, 63– 10. Web of Science®Google Scholar Ho, Y. C., and R. C. K. Lee (1964). A Bayesian approach to problems in stochastic estimation and control, IEEE Trans. Automatic Control, 9, 333– 339. CrossrefWeb of Science®Google Scholar Hoerl, A. E. (1962). Application of ridge regression to regression problems, Chem. Eng. Prog., 58, 54– 59. Google Scholar Hoerl, A. E., and R. W. Kennard (1970a). Ridge regression: biased estimation for non-orthogonal problems, Technometrics, 12, 55– 67. CrossrefWeb of Science®Google Scholar Hoerl, A. E., and R. W. Kennard (1970b). Ridge regression: applications to non-orthogonal problems, Technometrics, 12. 68– 82; correction, 12, p. 723. Google Scholar Hogg, R. V., and A. T. Craig (1978). Introduction to Mathematical Statistics, 4th ed., Macmillan, New York. Google Scholar Holt, C. C. (1957). Forecasting trends and seasonals by exponentially weighted moving averages, O. N. R. Memorandum, No. 52, Carnegie Institute of Technology. Google Scholar Houston, F. S., and D. L. Weiss (1975). Cumulative advertising effects: the role of serial correlation, Decision Sci., 6, 471– 481. Wiley Online LibraryGoogle Scholar Kalman, R. E. (1960). A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 35– 45. CrossrefGoogle Scholar Kalman, R. E., and R. S. Bucy (1961). New results in linear filtering and prediction theory, J. Basic Eng., 83, 95– 107. Google Scholar Kendall, M. G. (1976). Time Series, 2nd ed., Griffin & Co., London. Google Scholar Ledolter, J. (1975). Topics in time series analysis, unpublished Ph.D. dissertation, University of Wisconsin, Madison. Google Scholar Ledolter, J. (1979). A recursive approach to parameter estimation in regression and time series models, Commun. Statist., A8, 1227– 1245. CrossrefGoogle Scholar Ledolter, J. (1981). Recursive estimation and adaptive forecasting in ARIMA models with time varying coefficients, in Applied Time Series II ( D. F. Findley, Ed.), Academic Press, New York, pp. 449– 472. Google Scholar Ledolter, J., and B. Abraham (1981). Parsimony and its importance in time series forecasting, Technometrics, 23, 411– 414. CrossrefWeb of Science®Google Scholar Ledolter, J., and B. Abraham (1983). Some comments on the initialization of exponential smoothing, J. of Forecasting, 2, (forthcoming). Google Scholar Ledolter, J., and G. E. P. Box (1978). Conditions for the optimality of exponential smoothing forecast procedures, Metrika, 25, 77– 93. CrossrefGoogle Scholar Ledolter, J., and D. R. Kahl (1982). An empirical evaluation of adaptive filtering, Proceedings of American Institute for Decision Sciences, San Francisco, Vol. 2, 342– 344. Google Scholar Levinson, N. (1946). The Wiener RMS (root mean square) error criterion in filter design and prediction, J. Math. Phys., 25, 261– 278. Wiley Online LibraryWeb of Science®Google Scholar Ljung, G. M., and G. E. P. Box (1978). On a measure of lack of fit in time series models, Biometrika, 6, 297– 303. CrossrefWeb of Science®Google Scholar Ljung, G. M., (1979). The likelihood function of stationary autoregressive-moving average models. Biometrika, 66, 265– 270. CrossrefWeb of Science®Google Scholar Lovell, M. C. (1963). Seasonal adjustment of economic time series and multiple regression analysis, J. Amer. Statist. Assoc., 58, 993– 1010. CrossrefWeb of Science®Google Scholar Makridakis, S., and S. C. Wheelwright (1977). Adaptive filtering: an integrated autoregressive/moving average filter for time series forecasting, Oper. Res. Quart., 28, 425– 437. CrossrefWeb of Science®Google Scholar Makridakis, S., (1978). Forecasting—Methods and Applications, Wiley, New York. Google Scholar Mallows, C. L. (1973). Some Comments on Cp, Technometrics, 15, 661– 675. CrossrefWeb of Science®Google Scholar Marquardt, D. W. (1963). An algorithm for least squares estimation of non-linear models, J. Soc. Ind. Appl. Math., 11, 431– 441. CrossrefCASWeb of Science®Google Scholar Martin, R. D. (1980). Robust estimation of autoregressive models, in Directions in Time Series ( D. R. Brillinger and G. C. Tiao, Eds.), Institute of Mathematical Statistics, Hayward, CA, pp. 228– 254. Web of Science®Google Scholar McKenzie, E. (1974). A comparison of standard forecasting systems with the Box-Jenkins approach, Statistician, 23, 107– 116. Wiley Online LibraryWeb of Science®Google Scholar McKenzie, E. (1976). An analysis of general exponential smoothing, Oper. Res., 24, 131– 140. CrossrefWeb of Science®Google Scholar McLeod, A. I. (1977a). Topics in time series and econometrics, unpublished PhD. dissertation, University of Waterloo, Waterloo, Ontario. Google Scholar McLeod, A. I. (1977b). Improved Box-Jenkins estimators, Biometrika, 64, 531– 534. CrossrefWeb of Science®Google Scholar McLeod, A. I. (1978). On the distribution of residual autocorrelations in Box-Jenkins models, J. Roy. Statist. Soc., Ser. B, 40, 296– 302. Wiley Online LibraryWeb of Science®Google Scholar Mehra, R. K. (1979). Kalman filters and their applications to forecasting, in TIMS Studies in the Management Sciences; Vol. 12, Forecasting ( S. Makridakis and S. C. Wheelwright, Eds.), North-Holland, Amsterdam, pp. 75– 94. Web of Science®Google Scholar Mesnage, M. (1968). Elimination des variations saisonnieres: la nouvelle methode de l'OSCE. Etudes et Enquetes Statistiques, 1, 7– 78. Google Scholar Montgomery, D. C., and L. A. Johnson (1976). Forecasting and Time Series Analysis, McGraw-Hill, New York. Google Scholar Muth, J. F. (1960). Optimal properties of exponentially weighted forecasts, J. Amer. Statist. Assoc., 55, 299– 306. Web of Science®Google Scholar Narula, S. C., and J. F. Wellington (1977). Prediction, linear regression and the minimum sum of relative errors, Technometrics, 19, 185– 190. CrossrefWeb of Science®Google Scholar Nau, R. F., and R. M. Oliver (1979). Adaptive filtering revisited, J. Oper. Res. Soc., 30, 825– 831. CrossrefWeb of Science®Google Scholar Neter, J., and W. Wasserman (1974). Applied Linear Statistical Models, Irwin, Homewood, IL. Google Scholar Newbold, P. (1974). The exact likelihood function for a mixed autoregressive moving average process, Biometrika, 61, 423– 426. CrossrefWeb of Science®Google Scholar Palda, K. (1964). The Measurement of Cumulative Advertising Effects, Prentice-Hall, Englewood Cliffs, NJ. Web of Science®Google Scholar Pearson, E. S., and H. O. Hartley (1966). Biometrika Tables for Statisticans, Volume I, 3rd ed., Cambridge University Press, Cambridge. Google Scholar Pierce, D. A. (1978). Seasonal adjustment when both deterministic and stochastic seasonality are present, in Seasonal Analysis of Economic Time Series ( A. Zellner, Ed.), U. S. Department of Commerce Bureau of the Census, Washington, DC, pp. 242– 269. Web of Science®Google Scholar Plackett, R. L. (1950). Some theorems on least squares, Biometrika, 37, 149– 157. CrossrefCASPubMedWeb of Science®Google Scholar Plosser, C. (1979). Short-term forecasting and seasonal adjustment, J. Amer. Statist. Assoc., 74, 15– 24. Web of Science®Google Scholar Pollay, R. W. (1979). Lydiametrics: applications of econometrics to the history of advertising, J. Advert. Hist., 1, 3– 18. Google Scholar Rao, C. R. (1965). Linear Statistical Inference and Its Applications, Wiley, New York. Google Scholar Ryan, T. A., B. L., Joiner, and B. F. Ryan (1976). MINITAB Student Handbook, Duxbury Press, North Scituate, MA. Google Scholar Schweppe, F. C. (1965). Evaluation of likelihood functions for Gaussian signals, IEEE Trans. Inform. Theory, 11, 61– 70. CrossrefWeb of Science®Google Scholar S. M. Selby (Ed.) (1965). Standard Mathematical Tables, 14th ed., Chemical Rubber Company, Cleveland, OH. Google Scholar Shiskin, J., A. H., Young, and J. C. Musgrave (1967). The X-11 variant of the Census Method-II Seasonal Adjustment Program, Tech. Paper No. 15, U. S. Department of Commerce, Bureau of the Census, Washington, DC. Google Scholar Theil, H. (1971). Principles of Econometrics, Wiley, New York. Google Scholar Tiao, G. C., and G. E. P. Box (1981). Modeling multiple time series with applications, J. Amer. Statist. Assoc., 76, 802– 816. CrossrefWeb of Science®Google Scholar Tiao, G. C., G. E. P., Box, M. R. Grupe, O. B. Hudak, W. R. Bell, and I. Chang (1979). The Wisconsin Multiple Time Series (WMTS-I) Program: A Preliminary Guide, Department of Statistics, University of Wisconsin, Madison, WI. Google Scholar Tiao, G. C., and S. C. Hillmer (1978). Some considerations of decomposition of a time series, Biometrika, 65, 497– 502. CrossrefWeb of Science®Google Scholar Tintner, G. (1940). The Variate Difference Method, Principia Press, Bloomington, IN. Google Scholar Trigg, D. W., and A. G. Leach (1967). Exponential smoothing with an adaptive response rate, Oper. Res. Quart., 18, 53– 59. CrossrefWeb of Science®Google Scholar Tukey, J. W. (1961). Discussion emphasizing the connection between analysis of variance and spectrum analysis, Technometrics, 3, 189– 219. Google Scholar Wallis, K. F. (1974). Seasonal adjustment and relations between variables, J. Amer. Statist. Assoc., 69, 18– 31. CrossrefWeb of Science®Google Scholar Weiss, D. W., F. S., Houston, and P. Windal (1978). The periodic pain of Lydia E. Pinkham, J. Bus., 51, 91– 101. CrossrefWeb of Science®Google Scholar Whittle, P. (1963). Prediction and Regulation, Van Nostrand, New York. Google Scholar Whybark, D. C. (1973). A comparison of adaptive forecasting techniques, Logistics Transp. Rev., 9, 13– 26. Google Scholar Wichern, D. W. (1973). The behavior of the sample autocorrelation function for an integrated moving average process, Biometrika, 60, 235– 239. CrossrefWeb of Science®Google Scholar Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages, Manage. Sci., 6, 324– 342. CrossrefWeb of Science®Google Scholar Wold, H. (1938). A Study in the Analysis of Stationary Time Series ( 2nd ed. 1954), Almquist and Wicksell, Uppsala. Google Scholar Working, H. (1960). Note on the correlation of first differences of averages in a random chain. Econometrica, 28, 916– 918. CrossrefWeb of Science®Google Scholar Yaglom, A. M. (1955). The correlation theory of processes whose nth difference constitute a stationary process, Mat. Sb. N. S., 37 (79), 141– 196. Google Scholar Translated in Amer. Math. Soc. Transl., Ser. 2, 8 (1958), 87– 141. Google Scholar Yamamoto, F. (1981). Predictions of multivariate autoregressive moving average models, Biometrika, 68, 485– 492. CrossrefWeb of Science®Google Scholar Yule, G. U. (1921). On the time-correlation problems with special reference to the variatc difference correlation method, J. Roy. Statist. Soc., 84, 497– 526. CrossrefGoogle Scholar Yule, G. U. (1927). On a method of investigating periodicities in disturbed series, with special reference to Wolfer's sunspot numbers, Philos. Trans. Roy. Soc. London, Ser. A, 226, 267– 298. CrossrefGoogle Scholar Statistical Methods for Forecasting ReferencesRelatedInformation