REFERENCES
- Andrews, D.W. (1991), Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation, Econometrica, 817–858.
- Aston, J., Kirch, C. (2012), Estimation of the Distribution of Change-Points With Applications to fMRI Data, Annals of Applied Statistics, 6, 1906–1948.
- Aston, J., Kirch, C. (2013), Change Points in High-Dimensional Settings, discussion paper, KIT.
- Aue, A., Hörmann, S., Horváth, L., Reimherr, M. (2009), Break Detection in the Covariance Structure of Multivariate Time Series Model, The Annals of Statistics, 37, 4046–4087.
- Bai, J. (2000), Vector Autoregressive Models With Structural Changes in Regression Coefficients and in Variance-Covariance Matrices, Annals of Economics and Finance, 1, 303–339.
- Bai, J., Perron, L. (1998), Estimating and Testing Linear Models With Multiple Structural Changes, Econometrica, 66, 47–78.
- Benjamini, Y., Hochberg, J. (1995), Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society, Series B, 57, 289–300.
- Bickel, P., Bühlman, P. (1999), A New Mixing Notion and Functional Central Limit Theorems for a Sieve Bootstrap in Time Series, Bernoulli, 5, 413–446.
- Brockwell, P., and Davis, R. (1991), Time Series: Theory and Methods (2nd ed.), New York: Springer.
- Cho, H., Fryzlewicz, P. (2015), Multiple Change-Point Detection for High-Dimensional Time Series via Sparsified Binary Segmentation, Journal of the Royal Statistical Society, Series B, 77, 475–507.
- Chow, Y., and Teicher, H. (1997), Probability Theory—Independence, Interchangeability, Martingales (3rd ed.), New York: Springer.
- Csörgő, S., and Horváth, L. (1997), Limit Theorems in Change-Point Analysis, Chichester: Wiley.
- Davis, R., Huang, D., Yao, Y. (1995), Testing for a Change in the Parameter Values and Order of an Autoregressive Model, The Annals of Statistics, 23, 282–304.
- Davis, R., Lee, T., Rodriguez-Yam, G. (2006), Structural Break Estimation for Nonstationary Time Series Models, Journal of the American Statistical Association, 101, 223–229.
- Dvorak, M., Praskova, Z. (2013), On Testing Changes in Autoregressive Parameters of a VAR Model, Communications in Statistics: Theory and Methods, 42, 1208–1226.
- Fan, J., and Yao, Q. (2003), Nonlinear Time Series: Nonparametric and Parametric Methods, New York: Springer.
- Hlávka, Z., Hušková, M., Kirch, C., Meintanis, S.G. (2012), Monitoring Changes in the Error Distribution of Autoregressive Models Based on Fourier Methods, Test, 21, 605–634.
- Horváth, L. (1993), Change in Autoregressive Processes, Stochastic Processes and Their Applications, 5, 221–242.
- Hušková, M., Kirch, C., Prášková, Z., Steinebach, J. (2008), On the Detection of Changes in Autoregressive Time Series, II. Resampling Procedures, Journal of Statistical Planning and Inference, 138, 1697–1721.
- Hušková, M., Prášková, Z., Steinebach, J. (2007), On the Detection of Changes in Autoregressive Time Series, I. Asymptotics, Journal of Statistical Planning and Inference, 137, 1243–1259.
- Hušková, M., Slabý, A. (2001), Permutation Test for Multiple Changes, Kybernetika, 37, 606–622.
- Kirch, C., Muhsal, B. (2013), A MOSUM Procedure for The Estimation of Multiple Random Change Points, discussion paper, KIT.
- Kuelbs, J., Philipp, W. (1980), Almost Sure Invariance Principles for Partial Sums of Mixing b-Valued Random Variables, Annals of Probability, 8, 1003–1036.
- Kulperger, R. (1985), On the Residuals of Autoregressive Processes and Polynomial Regression, Stochastic Processes and Their Applications, 21, 107–118.
- Lesage, P., Glangeaud, F., Mars, J. (2002), Applications of Autoregressive Models and Time Frequency Analysis to the Study of Volcanic Tremor and Long-Period Events, Journal of Volcanology and Geothermal Research, 115, 391–417.
- Marconi, B., Genevesio, A., Battaglia-Meyer, A., Ferraina, S., Caminiti, R. (2001), Eye-Hand Coordination During Reaching: Anatomical Relationships Between the Parietal and Frontol Cortex, Cerebral Cortex, 11, 513–527.
- Marušiaková, M. (2009), Tests for Multiple Changes in Linear Regression Models. Ph.D. thesis, Charles University in Prague, Faculty of Mathematics and Physics.
- Pfurtscheller, G., Haring, G. (1972), The Use of an EEG Autoregressive Model for the Time-Saving Calculation of Spectral Power Density Distributions With a Digital Computer, Electroencephalography and Clinical Neurophysiology, 33, 113–115.
- Politis, D. (2011), Higher-Order Accurate, Positive Semi-Definite Estimation of Large-Sample Covariance and Spectral Density Matrices, Econometric Theory, 27, 703–744.
- Preuß, P., Puchstein, R., Dette, H. (2015), Detection of Multiple Structural Breaks in Multivariate Time Series, Journal of the American Statistical Association, 110, 654–668.
- Vermaak, J., Andrieu, C., Doucet, A., Godsill, S. (2002), Particle Methods for Bayesian Modeling and Enhancement of Speech Signals, IEEE Transactions on Speech and Audio Processing, 10, 173–185.