BIBLIOGRAPHY 279
Peprinted in Moder n Spectrum Anglysis, Childers, D.G. ed., I EEE
Press, New York(1978), 34–39.
[28] Carlin, B.P., Polson, N.G. and Stoffer, D.S. (1992), “A Monte Carlo
approa c h to nonnormal and non linear state space modeling” , J.
Amer. Statist. Assoc., 75, 493–500.
[29] Carter, C.K and Kohn, R. ( 1993), “A comparison of Markov chain
Monte Carlo sampling schemes for linear state space models”, in
Proceedings America n Statistical Association Business and Eco-
nomic Statistics Section, 131–136.
[30] Cleveland, W.S. and Devlin, S.J. (1980), “Calendar effects in
monthly time series: Detection by spectrum ana lysis and graphi-
cal methods”, J. Amer. Statist. Assoc., 75, 487–496.
[31] Cleveland, W.S., Devlin, S.J. and Terpenning, I. (1982), “The
SABL seasonal adjustment and calendar adjustment proc edures”,
Time Series Analysis: Th eory and Practice, 1, 539–564.
[32] Doucet, A., de Freitas, N. and G ordon, N. (2001), Sequential Monte
Carlo Methods in Practice, Springer, New York.
[33] Durbin, J. and Koopman, S.J. (2001), Time series analysis by state
space methods, Oxfo rd University Press, New York.
[34] Fletcher, R. (1980), Practical Method s of Optimization, 1: Uncon-
strained optimization, Wiley.
[35] Fr¨uhwirth-Schnatter, S. (1994), “Data augmentation and dynamic
linear models”, J. Time Series Anal., 183 –202.
[36] Golub, G . (1965), “Numerical methods for solving linear least-
square problems”, Num. Math., 7, 206–216.
[37] Good, L.J. and Gaskins, J.R. (1980), “De nsity estimation and bump
hunting by the penalized likelihood method exemplified by scatter-
ing and meteorite data”, J. Amer. Statist. Assoc., 75, 42–73.
[38] Gordon, N.J., Salmond, D.J. and Smith, A.F.M. (1993), “Novel ap-
proach to nonlinear/non-Gaussian Bayesian state estimation,” IEE
Proceedings–F, 140, 107–113.
[39] Harrison, P.J. and Stevens, C.F. (1976), “Bayesian forecasting”,
(with discussion), J. Royal Statist. Soc., B 38, 205–247.
[40] Harvey, A. (1989), Forecasting, Structural Time Series Models and
the Kalma n Filter, Cambridge University Press, Victoria, Australia.
[41] Hillmer, S.C. (1982), “Forecasting time series with trading day
variation”, J. Forecasting, 1, 385–395.