참고문헌
- Adams, R. and Mackay, D. (2007). Bayesian Online Changepoint Detection, University of Cambridge Technical Report.
- Barry, D. and Hartigan, J. (1993). A Bayesian analysis for change-point problems, Journal of the American Statistical Association, 88, 309-319.
- Bartels, R. (1982). The rank version of von Neumann's Ratio test for randomness, Journal of the American Statistical Association, 77, 40-46. https://doi.org/10.1080/01621459.1982.10477764
- Belisle, P., Joseph, L., MacGibbon, B., Wolfson, D. and du Berger, R. (1998). Change-point analysis of neuron spike train data, Biometrics, 54, 113-123. https://doi.org/10.2307/2534000
- Carlin, B., Gelfand, A. and Smith, A. F. M. (1992). Hierarchical Bayesian analysis of change point problems, Applied Statistics, 41, 389-405. https://doi.org/10.2307/2347570
- Cheon, S. and Kim, J. (2010). Multiple change-point detection of multivariate mean vectors with Bayesian approach, Computational Statistics & Data Analysis, 54, 406-415. https://doi.org/10.1016/j.csda.2009.09.003
- Chernoff, H. and Zacks, S. (1964). Estimating the current mean of a normal distribution which is subject to changes in time, Annals of Mathematical Statistics, 35, 999-1018. https://doi.org/10.1214/aoms/1177700517
- Chib, S. (1998). Estimation and comparison of multiple change-point models, Journal of Econometrics, 86, 221-241. https://doi.org/10.1016/S0304-4076(97)00115-2
- Corander, J., Gyllenberg, M. and Koski, T. (2009). Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy, Advanced Data Analysis and Classification, 3, 3-24. https://doi.org/10.1007/s11634-009-0036-9
- Cox, D. and Lewis, P. (1966). Methuen's Monographs on Applied Probability and Statistics, In The Statistical Analysis of Series of Events, John Wiley, London.
- Fearnhead, P. (2006). Exact and efficient Bayesian inference for multiple changepoint problems, Statistics and Computing, 16, 203-213. https://doi.org/10.1007/s11222-006-8450-8
- Fearnhead, P. and Clifford, P. (2003). Online inference for hidden Markov models, Journal of the Royal Statistical Society Series B, 65, 887-899. https://doi.org/10.1111/1467-9868.00421
- Gelfand, A. and Smith, A. (1990). Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association, 85, 398-409. https://doi.org/10.1080/01621459.1990.10476213
- Gelman, A., Carlin, J., Stern, H. and Rubin, D. (2003). Bayesian Data Analysis, Chapman and Hall, New York.
- Green, P. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, 82, 711-732. https://doi.org/10.1093/biomet/82.4.711
- Hastings, W. K. (1970). Monte Carlo sampling methods using Markov Chains and their applications, Biometrika, 57, 97-109. https://doi.org/10.1093/biomet/57.1.97
- Hawkins, D. (2001). Finding multiple change-point models to data, Computational Statistics & Data Analysis, 37, 323-341. https://doi.org/10.1016/S0167-9473(00)00068-2
- Hinkley, D. (1970). Inference about the change-point in a sequence of random variables, Biometrika, 57, 1-17. https://doi.org/10.1093/biomet/57.1.1
- Hinkley, D. (1972). Time-ordered classification, Biometrika, 59, 509-523. https://doi.org/10.1093/biomet/59.3.509
-
Holmes, D. and Mergen, A. (1993). Improving the performance of the
$T^2$ control chart, Quality Engineering, 5, 619-625. https://doi.org/10.1080/08982119308919004 - Hsu, D. (1979). Detecting shifts of parameter in gamma sequences with applications to stock price and air traffic flow analysis, Journal of the American Statistical Association, 74, 31-40. https://doi.org/10.1080/01621459.1979.10481604
- Jarrett, R. (1979). A note on the intervals between coal-mining disasters, Biometrika, 66, 191-193. https://doi.org/10.1093/biomet/66.1.191
- Kass, R. and Raftery, A. (1995). Bayes factors, Journal of the American Statistical Association, 90, 773-795. https://doi.org/10.1080/01621459.1995.10476572
- Kim, J. and Cheon, S. (2010). Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo, Computational Statistics, 25, 215-239. https://doi.org/10.1007/s00180-009-0172-x
- Kojadinovic, I. and Holmes, M. (2009). Tests of independence among continuous random vectors based on Cramer-von Mises functionals of the empirical copula process, Journal of Multivariate Analysis, 100, 1137-1154. https://doi.org/10.1016/j.jmva.2008.10.013
- Lee, C.-B. (1998). Bayesian analysis of a change-point in exponential families with applications, Computational Statistics & Data Analysis, 27, 195-208. https://doi.org/10.1016/S0167-9473(98)00009-7
- Liang, F., Liu, R. and Carroll, R. (2007). Stochastic approximation in Monte Carlo computation, Journal of the American Statistical Association, 102, 305-320. https://doi.org/10.1198/016214506000001202
- Maguire, B., Pearson, E. and Wynn, A. (1952). The time intervals between industrial accidents, Biometrika, 38, 168-180.
- Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087-1091. https://doi.org/10.1063/1.1699114
- O Ruanaidh, J. and Fitzgerald, W. (1996). Bayesian Online Changepoint Detection, Springer, New York.
- Raftery, A. (1995). Bayesian model selection in social research, In Marsden V (Ed) Sociological methodology, 25, 111-163. https://doi.org/10.2307/271063
- Raftery, A. and Akman, V. (1986). Bayesian analysis of a Poisson process with a change-point, Biometrika, 73, 85-89. https://doi.org/10.1093/biomet/73.1.85
- Robbins, H. and Monro, S. (1951). A stochastic approximation method, Annals of Mathematical Statistics, 22, 400-407. https://doi.org/10.1214/aoms/1177729586
- Siegmund D. (1988). Confidence sets in change point problem, International Statistical Review, 56, 31-48. https://doi.org/10.2307/1403360
- Smith, A. F. M. (1975). A Bayesian approach to inference about a change-point in a sequence of random variables, Biometrika, 62, 407-416. https://doi.org/10.1093/biomet/62.2.407
- Stephens, D. (1994). Bayesian retrospective multiple-change-point identification, Applied Statistics, 43, 159-178. https://doi.org/10.2307/2986119
- Sullivan, J. and Woodall, W. (2000). Change-point detection of mean vector or covariance matrix shifts using multivariate individual observations, IIE Transactions, 32, 537-549.
- Venter, J. and Steel, S. (1996). Finding multiple abrupt change-points, Computational Statistics & Data Analysis, 22, 481-504. https://doi.org/10.1016/0167-9473(96)00007-2
- Wehrens, R., Buydens, L. M. C., Fraley, C. and Raftery, A. E. (2004). Model-based clustering for image segmentation and large datasets via sampling, Journal of Classification, 21, 231-253. https://doi.org/10.1007/s00357-004-0018-8
- Wu, Y. (2005). Inference for change point and post change means after a CUSUM test, Lecture notes in Statistics, 180.
- Yao, Y. (1984). Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches, Annals of Statistics, 12, 1434-1447. https://doi.org/10.1214/aos/1176346802
피인용 문헌
- Bayesian Change Detection in the Growing Window Recursive Strategy vol.775, pp.1662-7482, 2015, https://doi.org/10.4028/www.scientific.net/AMM.775.399
- A note on the test for the covariance matrix under normality vol.25, pp.1, 2018, https://doi.org/10.29220/CSAM.2018.25.1.071