- Alwan, L. C., & Roberts, H. V. (1988). Time‐series modeling for statistical process control. Journal of Business & Economic Statistics, 6(1), 87–95.
Paper not yet in RePEc: Add citation now
- Anderson, M. J., & Thompson, A. A. (2004). Multivariate control charts for ecological and environmental monitoring. Ecological Applications, 14(6), 1921–1935.
Paper not yet in RePEc: Add citation now
Andersson, E., Bock, D., & Frisén, M. (2004). Detection of turning points in business cycles. Journal of Business Cycle Measurement and Analysis, 1, 93–108.
- Aue, A., Cheung, R. C., Lee, T. C., & Zhong, M. (2017). Piecewise quantile autoregressive modeling for nonstationary time series. Bernoulli, 23(1), 1–22.
Paper not yet in RePEc: Add citation now
Aue, A., Horváth, L., & Pellatt, D. (2017). Functional generalized autoregressive conditional heteroskedasticity. Journal of Time Series Analysis, 38(1), 3–21.
- Bai, Z., & Saranadasa, H. (1996). Effect of high dimension: By an example of a two sample problem. Statistica Sinica, 6, 311–329.
Paper not yet in RePEc: Add citation now
- Barone, S., & Chakhunashvili, A. (2022). Pandemetrics: Systematically assessing, monitoring, and controlling the evolution of a pandemic. Quality & Quantity, 1–23.
Paper not yet in RePEc: Add citation now
- Bersimis, S., Sgora, A., & Psarakis, S. (2018). The application of multivariate statistical process monitoring in non‐industrial processes. Quality Technology & Quantitative Management, 15(4), 526–549.
Paper not yet in RePEc: Add citation now
Bodnar, O. (2009). Sequential surveillance of the tangency portfolio weights. International Journal of Theoretical and Applied Finance, 12(6), 797–810.
- Bodnar, O., & Schmid, W. (2005). Multivariate control charts based on a projection approach. Allgemeines Statistisches Archiv, 89(1), 75–93.
Paper not yet in RePEc: Add citation now
Bodnar, O., & Schmid, W. (2007). Surveillance of the mean behavior of multivariate time series. Statistica Neerlandica, 61(4), 383–406.
- Bodnar, O., & Schmid, W. (2011). Cusum charts for monitoring the mean of a multivariate gaussian process. Journal of Statistical Planning and Inference, 141(6), 2055–2070.
Paper not yet in RePEc: Add citation now
- Bodnar, O., Bodnar, T., & Okhrin, Y. (2014). Robust surveillance of covariance matrices using a single observation. Sankhya A, 76(2), 219–256.
Paper not yet in RePEc: Add citation now
Bodnar, T., & Reiß, M. (2016). Exact and asymptotic tests on a factor model in low and large dimensions with applications. Journal of Multivariate Analysis, 150, 125–151.
Bodnar, T., Gupta, A. K., & Parolya, N. (2016). Direct shrinkage estimation of large dimensional precision matrix. Journal of Multivariate Analysis, 146, 223–236.
- Chen, S. X., & Qin, Y.‐L. (2010). A two‐sample test for high‐dimensional data with applications to gene‐set testing. The Annals of Statistics, 38(2), 808–835.
Paper not yet in RePEc: Add citation now
- Chen, S., & Nembhard, H. B. (2011). A high‐dimensional control chart for profile monitoring. Quality and Reliability Engineering International, 27(4), 451–464.
Paper not yet in RePEc: Add citation now
Chudik, A., & Pesaran, M. H. (2013). Econometric analysis of high dimensional vars featuring a dominant unit. Econometric Reviews, 32(5‐6), 592–649.
- Crosier, R. (1988). Multivariate generalizations of cumulative sum quality‐control schemes. Technometrics, 30, 291–303.
Paper not yet in RePEc: Add citation now
Dette, H., & Dörnemann, N. (2020). Likelihood ratio tests for many groups in high dimensions. Journal of Multivariate Analysis, 178, 104605.
Dias, G. F., & Kapetanios, G. (2018). Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202(1), 75–91.
- Frisén, M. (1992). Evaluations of methods for statistical surveillance. Statistics in Medicine, 11, 1489–1502.
Paper not yet in RePEc: Add citation now
- Golosnoy, V., & Schmid, W. (2007). Ewma control charts for monitoring optimal portfolio weights. Sequential Analysis, 26(2), 195–224.
Paper not yet in RePEc: Add citation now
- Golosnoy, W., Okhrin, I., Ragulin, S., & Schmid, W. (2011). On the application of SPC in finance. Frontiers in Statistical Quality Control, 9, 119–132.
Paper not yet in RePEc: Add citation now
Gupta, A., & Robinson, P. M. (2015). Inference on higher‐order spatial autoregressive models with increasingly many parameters. Journal of Econometrics, 186(1), 19–31.
Gupta, A., & Robinson, P. M. (2018). Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension. Journal of Econometrics, 202(1), 92–107.
- Hall, E. C., Raskutti, G., & Willett, R. (2016). Inferring high‐dimensional Poisson autoregressive models. Proceedings of the Statistical Signal Processing Workshop (SSP) (pp. 1–5). IEEE.
Paper not yet in RePEc: Add citation now
- Han, F., & Liu, H. (2013). Transition matrix estimation in high dimensional time series. Proceedings of the International Conference on Machine Learning (pp. 172–180).
Paper not yet in RePEc: Add citation now
- Han, F., Lu, H., & Liu, H. (2015). A direct estimation of high dimensional stationary vector autoregressions. Journal of Machine Learning Research, 16, 3115–3150.
Paper not yet in RePEc: Add citation now
- Hotelling, H. (1947). Multivariate quality control – illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. Wallis (Eds.), Techniques of statistical analysis (pp. 111–184). McGraw Hill.
Paper not yet in RePEc: Add citation now
Kock, A. B., & Callot, L. (2015). Oracle inequalities for high dimensional vector autoregressions. Journal of Econometrics, 186(2), 325–344.
- Kramer, H. G., & Schmid, L. (1997). Ewma charts for multivariate time series. Sequential Analysis, 16(2), 131–154.
Paper not yet in RePEc: Add citation now
Lam, C., & Yao, Q. (2012). Factor modeling for high‐dimensional time series: Inference for the number of factors. The Annals of Statistics, 40(2), 694–726.
- Lawson, A., & Kleinman, K. (2005). Spatial & syndromic surveillance. Wiley.
Paper not yet in RePEc: Add citation now
- Li, Y., Liu, Y., Zou, C., & Jiang, W. (2014). A self‐starting control chart for high‐dimensional short‐run processes. International Journal of Production Research, 52(2), 445–461.
Paper not yet in RePEc: Add citation now
- Li, Z., Zou, C., Gong, Z., & Wang, Z. (2014). The computation of average run length and average time to signal: An overview. Journal of Statistical Computation and Simulation, 84(8), 1779–1802.
Paper not yet in RePEc: Add citation now
- Liu, H., Aue, A., & Paul, D. (2015). On the marčenko–pastur law for linear time series. The Annals of Statistics, 43(2), 675–712.
Paper not yet in RePEc: Add citation now
- Lowry, C., Woodall, W., Champ, C., & Rigdon, S. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34, 46–53.
Paper not yet in RePEc: Add citation now
- Mathai, A. M., & Provost, S. B. (1992). Quadratic forms in random variables: Theory and applications. Dekker.
Paper not yet in RePEc: Add citation now
Messaoud, A., Weihs, C., & Hering, F. (2008). Detection of chatter vibration in a drilling process using multivariate control charts. Computational Statistics & Data Analysis, 52, 3208–3219.
Meyer, S., Held, L., & Höhle, M. (2017). Spatio‐temporal analysis of epidemic phenomena using the r package surveillance. Journal of Statistical Software, 77(11), 1–55.
- Montgomery, D. C. (2020). Introduction to statistical quality control. John Wiley & Sons.
Paper not yet in RePEc: Add citation now
- Ngai, H., & Zhang, J. (2001). Multivariate cumulative sum control charts based on projection pursuit. Statistica Sinica, 11, 747–766.
Paper not yet in RePEc: Add citation now
- Pignatiello, J., & Runger, G. (1990). Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22, 173–186.
Paper not yet in RePEc: Add citation now
- Reinsel, G. (1993). Multivariate time series analysis. John Wiley & Sons.
Paper not yet in RePEc: Add citation now
Schiöler, L., & Frisén, M. (2012). Multivariate outbreak detection. Journal of Applied Statistics, 39(2), 223–242.
- Schipper, S., & Schmid, W. (2001). Sequential methods for detecting changes in the variance of economic time series. Sequential Analysis, 20, 235–262.
Paper not yet in RePEc: Add citation now
- Schmid, W. (1995). On the run length of a Shewhart chart for correlated data. Statistical Papers, 36(1), 111–130.
Paper not yet in RePEc: Add citation now
- Schmid, W., & Tzotchev, D. (2004). Statistical surveillance of the parameters of a one‐factor Cox‐Ingersoll‐Ross model. Sequential Analysis, 23, 379–412.
Paper not yet in RePEc: Add citation now
- Shmueli, G., & Burkom, H. (2010). Statistical challenges facing early outbreak detection in biosurveillance. Technometrics, 52(1), 39–51.
Paper not yet in RePEc: Add citation now
Sonesson, C., & Bock, D. (2003). A review and discussion of prospective statistical surveillance in public health. Journal of the Royal Statistical Society A, 166, 5–21.
- Theodossiou, P. T. (1993). Predicting shifts in the mean of a multivariate time series process: An application in predicting business failures. Journal of the American Statistical Association, 88(422), 441–449.
Paper not yet in RePEc: Add citation now
- Wang, K., & Jiang, W. (2009). High‐dimensional process monitoring and fault isolation via variable selection. Journal of Quality Technology, 41(3), 247.
Paper not yet in RePEc: Add citation now
- Wang, L., Aue, A., & Paul, D. (2017a). Spectral analysis of sample autocovariance matrices of a class of linear time series in moderately high dimensions. Bernoulli, 23(4A), 2181–2209.
Paper not yet in RePEc: Add citation now
- Wang, Z., Li, Y., & Zhou, X. (2017b). A statistical control chart for monitoring high‐dimensional Poisson data streams. Quality and Reliability Engineering International, 33(2), 307–321.
Paper not yet in RePEc: Add citation now
- Ye, N., & Chen, Q. (2001). An anomaly detection technique based on a chi‐square statistic for detecting intrusions into information systems. Quality and Reliability Engineering International, 17(2), 105–112.
Paper not yet in RePEc: Add citation now