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Articles in Proceedings (Sortieren nach: Erscheinungsdatum | Titel)
Steland, A. (2001). On nonparametric sequential control for time series under mixing conditions with applications in finance and econometrics (peer-reviewed). 4th St. Petersburg Workshop on Simulation, St. Petersburg, Russia: St. Petersburg State University.
Steland, A. (2001). Measuring credit risk: Can we benefit from sequential nonparametric control? (Selected Papers, peer-reviewed). Proceedings of the 2001 Symposium on Operations Research, Duisburg, Germany: University of Duisburg.
Steland, A. (2005). On detection of unit roots generalizing the classic Dickey-Fuller approach (peer-reviewed). 5th St. Petersburg Workshop on Simulation, St. Petersburg, Russia: St. Petersburg State University.. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/stpetersburg2005.pdf
Steland, A. (2006). On monitoring a sequential linear LS residual process for integrated errors (655-665, peer-reviewed). Prague Stochastics 2006, 7th Prague Symposium on Asymptotic Statistics: MATFYZPRESS.. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/prague2006.pdf
Herrmann, W., Althaus, J., Steland, A. & Zähle, H. (2006). Statistical and experimental methods for assessing the power output specification of PV modules. Proceedings of the 21st European Photovoltaic Solar Energy Conference, Dresden, 2416-2420. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/TÜV_Proc.pdf
Steland, A. (2009). A note on data-adaptive bandwidth selection for sequential kernel smoothers (peer-reviewed). Proceedings of the 6th St. Petersburg Workshop on Simulation, 679-684: St. Petersburg State University.. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/StPetersburg2009.pdf
Steland, A. (2009). On Local Linear Surveillance of Trend Stability and Asymptotic Results for the Stopped Detectors (invited paper, peer-reviewed). Proceedings of the 2nd International Workshop in Sequential Methodologies: Troyes.. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/IWSM2009Steland.pdf
Rafajlowicz, E., Pawlak, M. & Steland, A. (2009). Nonparametric Sequential Change-Point Detection by a Vertical Regression Method [peer-reviewed, DOI: 10.1109/SSP.2009.5278502 ]. 15th IEEE/SP Workshop on Statistical Signal Processing, , 614-617. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/SSP2009vert.pdf
Herrmann, W., Steland, A. & Herff, W. (2010). Sampling Procedures for the Validation of PV Module Output Specifcation [DOI: 10.4229/24thEUPVSEC2009-4AV.3.70]. Proceedings of the 24th European Photovoltaic Solar Energy Conference, Hamburg, Germany, ISBN 3-936338-25-6, 3540-3547. Verfügbar unter http://www.eupvsec-proceedings.com/proceedings?fulltext=Steland&paper=5308
Pawlak, M. & Steland, A. (2011). Joint signal sampling and detection. Proceedings SampTA 2011, accepted
Steland, A. (2014a). Detection of changes in dependent processes: Learning from algorithms, simulations and stochastic inference. Advances in Applied and Pure Mathematics, Mathematics and Computers in Science and Engineering Series, 27, 77-81
Sovetkin, Evgenii & Steland, Ansgar (2015a). On statistical preprocessing of PV field image data using robust regression. In: Nikos E. Mastorakis, Adam Ding & Marina V. Shitikova (Hrsg.), Advances in Mathematics and Statistical Sciences, Vol. 40 (ISSN 2227-4588) (S.48-51). Dubai, United Arabian Emirates: WSEAS Press.. Verfügbar unter
Rafajlowicz, E. & Steland, A. (2019a). The Hotelling-like T2 control chart modified for detecting changes in images having the matrix normal distribution (In: Stochastic Models, Statistics and Their Applications (Springer Proceedings in Mathematics and Statistics, Volume 294)). Heidelberg: Springer.
Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M. & Mies, F. (2024b). Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning [AISTATS]. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, accepted
Besprechungen (Sortieren nach: Erscheinungsdatum | Titel)
Steland, A (2006). Book review on: Fitting Models to Biological Data Using Linear and Nonlinear Regression [Motulsky, H. and Christopoulos, A.]. Biometrical Journal, 48 (2), 237
Steland, A. (2008). Book review on: Angewandte Statistik I [Precht, Kraft, and Bechmaier]. Biometrical Journal, 50 (3), 452
Bücher (Sortieren nach: Erscheinungsdatum | Titel)
Steland, A. (2004). Mathematische Grundlagen der empirischen Forschung. Berlin, Heidelberg: Springer.. Verfügbar unter http://www.springer.com/dal/home?SGWID=1-102-22-3067004-0
Steland, A. (2007, 2010, 2013). Basiswissen Statistik - Kompaktkurs für Anwender aus Wirtschaft, Informatik und Technik. (3. (erw.) Auflage). Berlin: Springer.. Verfügbar unter http://www.springer.com/dal/home/statistics/business?SGWID=1-10135-22-173754558-0
Steland, A. (2012). Financial Statistics and Mathematical Finance: Methods, Models and Applications (415 pages. Visit the accompanying website). Chichester: John Wiley & Sons.. Verfügbar unter http://fsmf.stochastik.rwth-aachen.de/
Steland, Ansgar, Szajowski, Krzysztof & Rafajlowicz, Ewaryst (2015d). Stochastic Models, Statistics and Their Applications (Springer Proceedings in Mathematics and Statistics, Vol 122, 490 pages, ISSN 2194-1009). Heidelberg: Springer.. Verfügbar unter http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-319-13880-0
Steland, Ansgar, Okhrin, Ostap & Rajajlowicz, Ewaryst (2019b). Stochastic Models, Statistics and Their Applications (Springer Proceedings in Mathematics and Statistics, Volume 294, 450 pages). Heidelberg: Springer.. Verfügbar unter http://doi.org/10.1007/978-3-030-28665-1
Steland, A. & Tsui, K.L. (2022a). Artificial Intelligence, Data Science and Big Data in Envrionmental Science and Technology: Challenges, Perspectives and Solutions. Heidelberg: Springer Nature, Switzerland.. Verfügbar unter http://https://link.springer.com/book/10.1007/978-3-031-07155-3
Chapters in Edited Volumes (Sortieren nach: Erscheinungsdatum | Titel)
Meisen, S., Pepelyshev, A. & Steland, A. (2012b). Quality assessment in the presence of additional data in photovoltaics. in: Frontiers in Statistical Quality Control, 9,
Steland, A. (2015b). Sampling Plans for Control-Inspection Schemes Under Independent and Dependent Sampling Designs With Applications to Photovoltaics [[accepted March 11th, 2014]]. Frontiers in Statistical Quality Control 11, 10, 287-317. Verfügbar unter http://arxiv.org/abs/1402.2468
Prause, Annabel & Steland, Ansgar (2015c). Detecting changes in spatial-temporal image data based on quadratic forms. In: Ansgar Steland, Krzysztof Szajowski & Ewaryst Rafajlowicz (Hrsg.), Stochastic Models, Statistics and Their Applications (S.139-147). Heidelberg: Springer.. Verfügbar unter
Steland, A. & Pieters, B. E. (2022b). One-round cross-validation and uncertainty determination for randomized neural networks with applications to mobile sensors. In: A. Steland & K.L. Tsui (Hrsg.), Artificial Intelligence, Big Data and Data Science in Statistics (S.4-24). Cham: Springer Nature, Switzerland.. Verfügbar unter
Steland, A. (2024a). On Extreme Value Asymptotics of Projected Sample Covariances in High Dimensions with Applications in Finance and Convolutional Networks. In: S. Knoth, Y. Okhrin & P. Otto (Hrsg.), Festschrift for Wolfgang Schmid (S. accepted). Berlin: Springer.. Verfügbar unter http://https://arxiv.org/abs/2310.08150
Zeitschriften (Sortieren nach: Erscheinungsdatum | Titel)
Steland, A. (1997). On a rank test in a two-factor model with varying dependent repeated measurements. Journal of Nonparametric Statistics, 8 (3), 215-235
Steland, A. (1998). Bootstrapping rank statistics. Metrika, 47 (3), 251-264
Steland, A., Bolle, F. & Braham, M. (1999). Differences in honesty in Europe? Remarks on Rankings: Measurement without theory. Homo Oeconomicus [continued by: Homo Oeconomicus: Journal of Behavioral and Institutional Economics], XVI (2), 205-217
Schmid, W. & Steland, A. (2000). Sequential control of non-stationary processes by nonparametric kernel control charts. Journal of the German Statistical Association, 84, 315-336
Steland, A. (2000). On robust GMM estimation with applications in economics and finance. Discussiones Mathematicae Probability and Statistics, 20, 63-83
Steland, A. (2002). A Bayesian view on detecting drifts by nonparametric methods. Economic Quality Control, 2, 177-186
Steland, A. (2002). Nonparametric monitoring of financial time series by jump-preserving estimators. Statistical Papers, 43 (3), 361-377
Steland, A. (2003). Jump-preserving monitoring of dependent processes using pilot estimators. Statistics and Decision, 21 (4), 343-366
Böhringer, S., Hardt, C., Miterski, B., Steland, A. & Epplen, J. T. (2003). Multilocus Statistics to Uncover Epistasis and Heterogeneity in Complex Diseases: Revisiting a Set of Multiple Sclerosis Data. European Journal of Human Genetics, 11, 573-584
Steland, A. (2004). NP-optimal kernels for nonparametric sequential detection rules. Economic Quality Control, 18 (2), 149-163
Pawlak, M., Rafajlowicz, E. & Steland, A. (2004). On detecting jumps in time series - Nonparametric setting. Journal of Nonparametric Statistics, 16, 329-347
Steland, A. (2004). Sequential control of time series by functionals of kernel-weighted empirical processes under local alternatives. Metrika, 60, 229-249
Steland, A. (2005). Random walks with drift - A sequential approach. Journal of Time Series Analysis, 26 (6), 917-942. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/rw_preprint_2005.pdf
Steland, A. (2005). Optimal sequential kernel smoothers under local nonparametric alternatives for dependent processes. Journal of Statistical Planning and Inference, 132, 131-147
Bart, A.G., Bart, V.G., Steland, A. & Zaslavskiy, M.L. (2005). Modeling disease dynamics and survivor functions by sanogenesis curves. Journal of Statistical Planning and Inference, 132, 33-51
Steland, A. (2005). On the distribution of the clipping median under a mixture model. Statistics and Probability Letters, 70 (1), 1-13
Steland, A. (2006). A bootstrap view on Dickey-Fuller control charts for AR(1) series. Austrian Journal of Statistics (35), 339-346. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/AJSsteland_TR.pdf
Biedermann, S., Nagel, E., Munk, A., Holzmann, H. & Steland, A. (2006). Tests in a case control design including relatives. Scandinavian Journal of Statistics, 33 (4), 621-635. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/statgen.pdf
Steland, A. (2007). Monitoring procedures to detect unit roots and stationarity [Software available (see 'Sonstiges')]. Econometric Theory, 23 (6), 1108-1135. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/sequnitroot.pdf
Steland, A. (2007). Weighted Dickey-Fuller processes for detecting stationarity. Journal of Statistical Planning and Inference, 137 (12), 4011-4030. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/seq-df-preprint.pdf
Pawlak, M., Rafajlowicz, E. & Steland, A. (2008). Nonlinear image filtering and reconstruction: a unified approach based on vertically weighted regression. International Journal of Applied Mathematics and Computer Science, 18 (1), 49-61. Verfügbar unter http://pldml.icm.edu.pl/pldml/element/bwmeta1.element.bwnjournal-article-amcv18i1p49bwm
Steland, A. (2008). Sequentially updated residuals and detection of stationary errors in polynomial regression models. Sequential Analysis, 27 (3), 304-329. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/sqa2008-preprint.pdf
Steland, A. & Zähle, H. (2009a). Sampling inspection by variables: nonparametric setting. Statistica Neerlandica, 63 (1), 101-123. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/TRsampling2.pdf
Rafajlowicz, E. & Steland, A. (2009b). A binary control chart to detect small jumps [DOI 10.1080/02331880802379405]. Statistics, 43 (3), 295-311. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/BinaryChart_TR.pdf
Steland, A. (2010a). A surveillance procedure for random walks based on local linear estimation. Journal of Nonparametric Statistics, 22 (3), 345-361. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/steland_loclin_rw preprint.pdf
Herrmann, W. & Steland, A. (2010b). Evaluation of Photovoltaic Modules Based on Sampling Inspection Using Smoothed Empirical Quantiles [ DOI: 10.1002/pip.926]. Progress in Photovoltaics, 18 (1), 1-9. Verfügbar unter http://www3.interscience.wiley.com/cgi-bin/fulltext/123227456/PDFSTART
Pawlak, M., Rafajlowicz, E. & Steland, A. (2010c). Nonparametric sequential change-point detection by a vertically trimmed box method [10.1109/TIT.2010.2048443]. IEEE Transactions on Information Theory, 56 (7), 3621-3634 . Verfügbar unter http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5484990
Steland, A. (2010d). Discussion on 'Quickest Detection Problems: Fifty Years Later' by A.N. Shiryaev (Steklov Mathematical Institute, Russian Academy of Sciences) [invited]. Sequential Analysis, 29 (4), 403-407
Steland, A, Padmanabhan, A.R. & Akram, M. (2011). Resampling methods for the nonparametric and generalized Behrens-Fisher problems. Sankhya Series A, 73-A (2), 267-302. Verfügbar unter http://sankhya.isical.ac.in/search/73a2/A09053_f.pdf
Steland, A. (2012a). Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction. Communications in Statistics, 41 (7), 1195-1219. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/SeqCV-03.pdf
Golyandina, N., Pepelyshev, A. & Steland, A. (2012c). New approaches to nonparametric density estimation and selection of smoothing parameters. Computational Statistics and Data Analysis, 56 (7), 2206-2218. Verfügbar unter http://www.sciencedirect.com/science/article/pii/S0167947311004543
Steland, A. (2012d). Sequential cross-validated bandwidth selection under dependence and Anscombe-type extensions to random time horizons [invited, in honor of F. Anscombe, Princeton, 1918-2001,http://en.wikipedia.org/wiki/Francis_Anscombe]. Sequential Analysis, 31 (3), 326-350
Pawlak, M. & Steland, A. (2013b). Nonparametric Sequential Signal Change Detection Under Dependent Noise [accepted December 12th. 2012]. IEEE Transactions on Information Theory, 59 (6 ), 3514-3531. Verfügbar unter http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6422391
Steland, A. & Weidauer, S. (2013c). Detection of stationary errors in multiple regressions with integrated regressors and cointegration [accepted April 27th, 2013]. Sequential Analysis, 32 ( 3), 319-349
Steland, A. & Rafajlowicz, E. (2014b). Decoupling change-point detection based on characteristic functions: Methodology, asymptotics, subsampling and application [accepted August 2nd, 2013]. Journal of Statistical Planning and Inference, 145, 49-73. Verfügbar unter http://www.isw.rwth-aachen.de/public/steland/preprints/ChF-18.pdf
Pepelyshev, A., Rafajlowicz, E. & Steland, A. (2014c). Estimation of the quantile function using Bernstein-Durrmeyer polynominals [accepted July10th, 2013]. Journal of Nonparametric Statistics, 145 ( ), 49-73
Pepelyshev, A., Steland, A. & Avellan-Hampe, A. (2014d). Acceptance sampling plans for photovoltaic modules with two-sided specification limits [accepted Sept 9, 2012]. Progress in Photovoltaics, 22 (6), 603-611
Steland, A. (2015e). Vertically weighted averages in Hilbert spaces and applications to imaging: Fixed sample asymptotics and efficient sequential two-stage estimation [Invited paper for the special Issue on Stein's 1945 AMS paper]. Sequential Analysis, 34 (3), 295-323
Prause, A., Steland, A. & Abujarad, M. (2016a). Minimum Hellinger Distance Estimation for Bivariate Samples and Time Series with Applications to Nonlinear Regression and Copula-Based Models. Metrika, 79 ( ), 425-455
Steland, A. (2016b). Asymptotics for random functions moderated by dependent noise [published online Dec 7th 2015]. Statistical Inference for Stochastic Processes, 19 (3), 363-387. Verfügbar unter http://link.springer.com/article/10.1007/s11203-015-9130-0?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst
Steland, A. (2016c). On the accuracy of fixed sample and fixed width confidence intervals based on the vertically weighted average. Journal of Statistical Theory and Practice, online Dec 6th. Verfügbar unter http://www.tandfonline.com/doi/abs/10.1080/15598608.2016.1263809?journalCode=ujsp20
Steland, A. (2017a). Fusing photovoltaic data for improved confidence intervals [Open Access]. AIMS Energy, 5 (1), 113-136. Verfügbar unter http://DOI 10.3934/energy.2017.1113
Pepelyshev, A., Sovetkin, E. & Steland, A. (2017b). Panel-Based Stratified Cluster Sampling and Analysis for Photovoltaic Outdoor Measurements [Open Access]. Applied Stochastic Models in Business and Industry, 33 (1), 35-53. Verfügbar unter http://onlinelibrary.wiley.com/doi/10.1002/asmb.2217/full
Steland, A. (2017c). Discussion on 'An Effective Method for the Explicit Solution of Sequential Problems on the Real Line' by S. Christensen. Sequential Analysis, 36, 30-32
Prause, A. & Steland, A. (2017d). Sequential Detection of Three-Dimensional Signals under Dependent Noise. Sequential Analysis, 36 (2), 151-178. Verfügbar unter http://arxiv.org/abs/1703.07303
Steland, A. & von Sachs, R. (2017e). Large sample approximations for variance-covariance matrices of high-dimensional time series. Bernoulli, 23 (4A), 2299-2329. Verfügbar unter http://arxiv.org/abs/1704.06230
Steland, A & von Sachs, R (2018a). Asymptotics for high-dimensional covariance matrices and quadratic forms with applications to the trace functional and shrinkage. Stochastic Processes and Their Applications, 128 (8), 2816-2855. Verfügbar unter http://arxiv.org/abs/1711.01835
Fischer, G., Hupach, U., Schmauder, J., Sepanski, A., Sommer, A., Steland, A. & Vaaßen, W. (2018b). Failure assessments of PV systems demonstrate the importance of elective quality assurance. PV-Tech Power, 14, 70-81. Verfügbar unter http://www.pv-tech.org/pv-tech-power/pv-tech-power-volume-14
Prause, A. & Steland, A. (2018c). Estimation of the asymptotic variance of univariate and multivariate random fields and statistical inference. Electronic Journal of Statistics, 12 (1), 890-940. Verfügbar unter http://projecteuclid.org/euclid.ejs/1520326826
Steland, A. (2018d). Convergence of moments for approximating processes and applications to surrogate models like deep learning neural networks [http://www.jyotiacademicpress.org/jyotic/journalview/35/article/50/83]. International Journal of Statistics: Advances in Theory and Applications, 2 (1), 77-94. Verfügbar unter http://arxiv.org/abs/1804.10821
Steland, A. (2018e). Shrinkage for covariance estimation: Asymptotics, confidence intervals, bounds and applications in sensor monitoring and finance. Statistical Papers, 59 (4), 1441-1462. Verfügbar unter http://arxiv.org/abs/1809.00463
Sommer, A. & Steland, A. (2019c). Multistage Acceptance Sampling under Nonparametric Dependent Sampling Designs. Journal of Statistical Planning and Inference, 199, 89-113
Sovetkin, E. & Steland, A. (2019d). Automatic processing and solar cell detection in photovoltaic electroluminescence images. Integrated Computer-Aided Engineering, 26 (2), 123-137. Verfügbar unter http://arxiv.org/abs/1807.10820
Mies, F. & Steland, A. (2019e). Nonparametric Gaussian inference for stable processes. Statistical Inference for Stochastic Processes, 22 (3), 525-555
Steland, A. & Chang, Y.T. (2019f). Jackknife variance estimation for common mean estimators under ordered variances and general two-sample statistics. Japanese Journal of Statistics and Data Science, 1 (2), 173-217. Verfügbar unter http://arxiv.org/abs/1710.01898
Steland, A. (2020a). Testing and estimating a change-point in the covariance matrix of a high-dimensional time series. Journal of Multivariate Analysis, 177, online
Mause, N. & Steland, A. (2020b). Detecting changes in the second moment structure of high-dimensional sensor-type data in a k-sample setting. Sequential Analysis, 39, 336-366. Verfügbar unter http://arxiv.org/abs/2001.05204
Chang, Y.T. & Steland, A. (2021a). High-confident nonparametric fixed-width uncertainty intervals and applications to projected high-dimensional data and common mean estimation. Sequential Analysis, 40 (1), 97-124. Verfügbar unter http://arxiv.org/abs/1910.02829
Bours, M. & Steland, A. (2021b). Large-sample approximations and change testing for high-dimensional covariance matrices of multivariate linear time series and factor models [Open access, first published December 14th 2020]. Scandinavian Journal of Statistics, 48 (2), 610-654. Verfügbar unter http://doi.org/10.1111/sjos.12508
Deitsch, S., Buerhop-Lutz, C., Sovetkin, E., Steland, A., Meier, A., Gallwitz, F. & Riess, C. (2021c). Segmentation of photovoltaic module cells in uncalibrated electroluminescence images machine vision and applications. Machine Vision and Applications, 32, Article No. 84. Verfügbar unter http://https://arxiv.org/abs/1806.06530
Loboda, D., Mies, F. & Steland, A. (2021d). Regularity of multifractional moving average processes with random Hurst exponent. Stochastic Processes and Their Applications, 140, 21-48. Verfügbar unter http://https://arxiv.org/abs/2004.07539
Friedrich, Sarah, Antes, Gerd, Behr, Sigrid, Binder, Harald, Brannath, Werner, Dumpert, Florian, Ickstadt, Katja, Kestler, Hans, Lederer, Johannes, Leitgöb, Heinz, Pauly, Markus, Steland, Ansgar, Wilhelm, Adalbert & Friede, Tim (2021e). Is there a role for statistics in artificial intelligence?. Advances in Data Analysis and Classification, 16 (4), 823-846. Verfügbar unter http://arxiv.org/abs/2009.09070
Greipel, Jonathan, Frank, Regina, Huber, Meike, Steland, Ansgar & Schmitt, Robert (2022c). Auto-Encoder-based Algorithm for the Selection of Key Characteristics for Products to Reduce Inspection Efforts. International Journal of Quality & Reliability Management, 40 (7), 1597-1620
Mies, F. & Steland, A. (2023). Sequential Gaussian approximation for nonstationary time series in high dimensions. Bernoulli, 29 (4), 3114-3140. Verfügbar unter http://https://arxiv.org/abs/2203.03237
Steland, A. (2024c). Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices. Journal of Multivariate Analysis, 199, 105245
Mies, F. & Steland, A. (2024d). Projection inference for high-dimensional covariance matrices with structured shrinkage targets. Electronic Journal of Statistics, 18 (1), 1643-1676. Verfügbar unter http://https://arxiv.org/abs/2211.02368
Steland, A. (2024e). Are minimum variance portfolios in multi-factor models long in low-beta assets?. Mathematics and Financial Economics (online), 1-20. Verfügbar unter http://https://link.springer.com/article/10.1007/s11579-024-00366-y
Steland, A., Rafajlowicz, E. & Rafajlowicz, W. (2024f). General adapted-threshold monitoring in discrete environments and rules for imbalanced classes. Statistica Neerlandica, accepted