Items where department is "Statistics"

University Structure (106206) LSE (106206) Academic Departments (62869) Statistics (1716)
Number of items: 99.
A
  • Aschermayr, Patrick (2023). Sequential Bayesian learning for State Space Models [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004578
  • Atkinson, Anthony C., Duarte, Belmiro P.M., Pedrosa, David, van Munster, Marlena (2023). Randomizing a clinical trial in neuro-degenerative disease. Contemporary Clinical Trials Communications, 33, https://doi.org/10.1016/j.conctc.2023.101140 picture_as_pdf
  • Chen, Yining, S. Torrent, Hudson, A. Ziegelmann, Flavio (2023). Robust nonparametric frontier estimation in two steps. Econometric Reviews, 42(7), 612 - 634. https://doi.org/10.1080/07474938.2023.2219183 picture_as_pdf
  • Duarte, Belmiro P.M., Atkinson, Anthony C., Oliveira, Nuno M.C. (2023). Optimum design for ill-conditioned models: K–optimality and stable parameterizations. Chemometrics and Intelligent Laboratory Systems, 239, https://doi.org/10.1016/j.chemolab.2023.104874 picture_as_pdf
  • Duarte, Belmiro P.M., Atkinson, Anthony C., P. Singh, Satya, S. Reis, Marco (2023). Optimal design of experiments for hypothesis testing on ordered treatments via intersection-union tests. Statistical Papers, 64(2), 587 - 615. https://doi.org/10.1007/s00362-022-01334-8 picture_as_pdf
  • Nasir, Nida, Kansal, Afreen, Alshaltone, Omar, Barneih, Feras, Shanableh, Abdallah, Al-Shabi, Mohammad, Al Shammaa, Ahmed (2023). Deep learning detection of types of water-bodies using optical variables and ensembling. Intelligent Systems with Applications, 18, https://doi.org/10.1016/j.iswa.2023.200222 picture_as_pdf
  • Riani, Marco, Atkinson, Anthony C., Corbellini, Aldo (2023). Automatic robust Box-Cox and extended Yeo-Johnson transformations in regression. Statistical Methods and Applications, 32(1), 75 - 102. https://doi.org/10.1007/s10260-022-00640-7 picture_as_pdf
  • Riani, Marco, Atkinson, Anthony C., Corbellini, Aldo (2023). Robust response transformations for generalized additive models via additivity and variance stabilization. In Grilli, Leonardo, Lupparelli, Monia, Rampichini, Carla, Rocco, Emilia, Vichi, Maurizio (Eds.), Statistical Models and Methods for Data Science (pp. 147 - 159). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-30164-3_12 picture_as_pdf
  • Wu, Qianxin, Wu, Junjing, Abdul Karim, Muhammad Kaiser, Chen, Xi, Wang, Tengyao, Iwama, Sho, Carobbio, Stefania, Keen, Peter, Vidal-Puig, Antonio & Kotter, Mark R. et al (2023). Massively parallel characterization of CRISPR activator efficacy in human induced pluripotent stem cells and neurons. Molecular Cell, 83(7), 1125 - 1139. https://doi.org/10.1016/j.molcel.2023.02.011 picture_as_pdf
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  • Barreto, Marcos (13 June 2023) A fundamental problem at the heart of data science teaching. LSE Higher Education Blog. picture_as_pdf
  • Baurdoux, Erik J., Pedraza, José M. (2023). Predicting the last zero before an exponential time of a spectrally negative Lévy process. Advances in Applied Probability, 55(2), 611 - 642. https://doi.org/10.1017/apr.2022.47 picture_as_pdf
  • Bonnier, Patric, Oberhauser, Harald, Szabo, Zoltan (2023). Kernelized cumulants: beyond kernel mean embeddings. In Advances in Neural Information Processing Systems 36 . Curran Associates, Inc.. picture_as_pdf
  • Bottazzi, Giulio, Cordoni, Francesco, Livieri, Giulia, Marmi, Stefano (2023). Uncertainty in firm valuation and a cross-sectional misvaluation measure. Annals of Finance, 19(1), 63 - 93. https://doi.org/10.1007/s10436-022-00423-w picture_as_pdf
  • Bynum, Lucius E.J., Loftus, Joshua R., Stoyanovich, Julia (2023). Counterfactuals for the future. In Williams, Brian, Chen, Yiling, Neville, Jennifer (Eds.), AAAI-23 Special Tracks (pp. 14144-14152). AAAI Press. https://doi.org/10.1609/aaai.v37i12.26655
  • Di Mari, Roberto, Bakk, Zsuzsa, Oser, Jennifer, Kuha, Jouni (2023). A two-step estimator for multilevel latent class analysis with covariates. Psychometrika, 88(4), 1144 - 1170. https://doi.org/10.1007/s11336-023-09929-2 picture_as_pdf
  • Mavridis, Dimitris, Nikolakopoulou, Adriani, Moustaki, Irini, Chaimani, Anna, Porcher, Raphaël, Boutron, Isabelle, Ravaud, Philippe (2023). Considering multiple outcomes with different weights informed the hierarchy of interventions in network meta-analysis. Journal of Clinical Epidemiology, 154, 188-196. https://doi.org/10.1016/j.jclinepi.2022.12.025 picture_as_pdf
  • Rudas, Tamás, Bergsma, Wicher (2023). Marginal models: an overview. In Kateri, Maria, Moustaki, Irini (Eds.), Trends and Challenges in Categorical Data Analysis: Statistical Modelling and Interpretation (pp. 67 - 115). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-31186-4_3 picture_as_pdf
  • Uehara, Masatoshi, Kiyohara, Haruka, Bennett, Andrew, Chernozhukov, Victor, Jiang, Nan, Kallus, Nathan, Shi, Chengchun, Sun, Wenguang (2023). Future-dependent value-based off-policy evaluation in POMDPs. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (Eds.), Advances in Neural Information Processing Systems 36 (NeurIPS 2023) . Neural Information Processing Systems Foundation. picture_as_pdf
  • van der Ark, L. Andries, Bergsma, Wicher P., Koopman, Letty (2023). Maximum augmented empirical likelihood estimation of categorical marginal models for large sparse contingency tables. Psychometrika, 88(4), 1228 - 1248. https://doi.org/10.1007/s11336-023-09932-7 picture_as_pdf
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  • Cai, Hanqing, Wang, Tengyao (2023). Estimation of high-dimensional change-points under a group sparsity structure. Electronic Journal of Statistics, 17(1), 858 – 894. https://doi.org/10.1214/23-EJS2116 picture_as_pdf
  • Cai, Hengrui, Shi, Chengchun, Song, Rui, Lu, Wenbin (2023). Jump interval-learning for individualized decision making with continuous treatments. Journal of Machine Learning Research, picture_as_pdf
  • Caron, François, Panero, Francesca, Rousseau, Judith (2023). On sparsity, power-law, and clustering properties of graphex processes. Advances in Applied Probability, 55(4), 1211 - 1253. https://doi.org/10.1017/apr.2022.75 picture_as_pdf
  • Chang, Jinyuan, Chen, Cheng, Qiao, Xinghao, Yao, Qiwei (2023). An autocovariance-based learning framework for high-dimensional functional time series. Journal of Econometrics, 239(2). https://doi.org/10.1016/j.jeconom.2023.01.007 picture_as_pdf
  • Chang, Jinyuan, Zhang, Henry, Yang, Lin, Yao, Qiwei (2023). Modelling matrix time series via a tensor CP-decomposition. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85(1), 127 – 148. https://doi.org/10.1093/jrsssb/qkac011 picture_as_pdf
  • Chen, Yunxiao, Xu, Gongjun (2023). Yunxiao Chen and Gongjun Xu's contribution to the discussion of ‘Vintage factor analysis with Varimax performs statistical inference’ by Rohe & Zeng. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85(4), 1082 – 1084. https://doi.org/10.1093/jrsssb/qkad040
  • Chen, Yunxiao, Li, Chengcheng, Ouyang, Jing, Xu, Gongjun (2023). DIF statistical inference without knowing anchoring items. Psychometrika, 88(4), 1097 - 1122. https://doi.org/10.1007/s11336-023-09930-9 picture_as_pdf
  • Chen, Yunxiao, Li, Chengcheng, Ouyang, Jing, Xu, Gongjun (2023). Statistical inference for noisy incomplete binary matrix. Journal of Machine Learning Research, 24, picture_as_pdf
  • Chen, Yunxiao, Li, Xiaoou (2023). Compound sequential change-point detection in parallel data streams. Statistica Sinica, 33(1), 453 - 474. https://doi.org/10.5705/ss.202020.0508 picture_as_pdf
  • Chen, Zezhun (2023). Point processes and integer-valued time series [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004552
  • Chen, Zezhun Chen, Dassios, Angelos, Tzougas, George (2023). INAR approximation of bivariate linear birth and death process. Journal of Applied Statistics, 26(3), 459 - 497. https://doi.org/10.1007/s11203-023-09289-9 picture_as_pdf
  • Chen, Zezhun Chen, Dassios, Angelos, Tzougas, George (2023). A first order binomial mixed poisson integer-valued autoregressive model with serially dependent innovations. Journal of Applied Statistics, 50(2), 352 - 369. https://doi.org/10.1080/02664763.2021.1993798 picture_as_pdf
  • Chiang, Daryl, Kotecha, Meena (2023-09-04 - 2023-09-07) Reducing mathematics anxiety by enhancing mathematical resilience - a mindset intervention [Poster]. RSS International Conference 2023, Harrogate Convention Centre, Harrogate, United Kingdom, GBR. picture_as_pdf
  • Cárdenas Hurtado, Camilo Alberto (2023). Generalised latent variable models for location, scale, and shape parameters [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004531
  • Goracci, Greta, Giannerini, Simone, Chan, Kung Sik, Tong, Howell (2023). Testing for threshold effects in the Tarma framework. Statistica Sinica, 33(3), 1879-1901. https://doi.org/10.5705/ss.202021.0120
  • Guastadisegni, Lucia, Moustaki, Irini, Vasdekis, Vassilis, Cagnone, Silvia (2023). Detecting latent variable non-normality through the generalized Hausman test. In Wiberg, Marie, Molenaar, Dylan, González, Jorge, Kim, Jee-Seon, Hwang, Heungsun (Eds.), Quantitative Psychology - The 87th Annual Meeting of the Psychometric Society, 2022 (pp. 107-118). Springer Netherlands. https://doi.org/10.1007/978-3-031-27781-8_10 picture_as_pdf
  • Hana, Yuefeng, Chenb, Rong, Zhangb, Cun-Hui, Yao, Qiwei (2023). Simultaneous decorrelation of matrix time series. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2022.2151448 picture_as_pdf
  • Li, Jing-Jing, Shi, Chengchun, Li, Lexin, Collins, Anne G.E. (2023-07-26 - 2023-07-29) A generalized method for dynamic noise inference in modeling sequential decision-making [Paper]. Cognition in context, International Convention Centre Sydney, Sydney, Australia, AUS.
  • Liu, Xinyi Lin, Wallin, Gabriel, Chen, Yunxiao, Moustaki, Irini (2023). Rotation to sparse loadings using Lp losses and related inference problems. Psychometrika, 88(2), 527 - 553. https://doi.org/10.1007/s11336-023-09911-y picture_as_pdf
  • Shen, Tonggaochuan, Cheng, Long, Yang, Yongjiang, Deng, Jialin, Jin, Tanhua, Cao, Mengqiu (2023). Do residents living in transit-oriented development station catchment areas travel more sustainably? The impacts of life events. Journal of Advanced Transportation, 2023, https://doi.org/10.1155/2023/9318505 picture_as_pdf
  • Xie, Zilong, Chen, Yunxiao, von Davier, Matthias, Weng, Haolei (2023). Variable selection in latent variable models via knockoffs: an application to international large-scale assessment in education. Journal of the Royal Statistical Society. Series A: Statistics in Society, https://doi.org/10.1093/jrsssa/qnad137 picture_as_pdf
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  • Dadush, Daniel, Koh, Zhuan Khye, Natura, Bento, Végh, László A. (2023). An accelerated Newton–Dinkelbach method and its application to two variables per inequality systems. Mathematics of Operations Research, 48(4), 1934 - 1958. https://doi.org/10.1287/moor.2022.1326 picture_as_pdf
  • Dassios, Angelos, Zhang, Junyi (2023). Exact simulation of Poisson-Dirichlet distribution and generalised gamma process. Methodology and Computing in Applied Probability, 25(2). https://doi.org/10.1007/s11009-023-10040-3 picture_as_pdf
  • Divasón, Jose, Mohammadi, Fatemeh, Saenz-De-Cabezon, Eduardo, Wynn, Henry (2023). Sensitivity analysis of discrete preference functions using Koszul simplicial complexes. In Jeronimo, Gabriela (Ed.), ISSAC 2023 - Proceedings of the 2023 International Symposium on Symbolic and Algebraic Computation (pp. 227-235). Association for Computing Machinery. https://doi.org/10.1145/3597066.3597095
  • Jang, Jiwook, Qu, Yan, Zhao, Hongbiao, Dassios, Angelos (2023). A Cox model for gradually disappearing events. Probability in the Engineering and Informational Sciences, 37(1), 214 - 231. https://doi.org/10.1017/S0269964821000553 picture_as_pdf
  • Luo, Yuanyuan (2023). Comparing recurrent neural network with GARCH model on forecasting volatility based on SSE 50ETF. In Dai, Wanyang, Jin, Shi (Eds.), Second International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022 . Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2673039
  • Luo, Yuanyuan (2023). Using GARCH family models estimate the volatility of SSE 50ETF. In Dai, Wanyang, Jin, Shi (Eds.), Second International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022 . Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2671961
  • Qu, Yan, Dassios, Angelos, Zhao, Hongbiao (2023). Shot-noise cojumps: exact simulation and option pricing. Journal of the Operational Research Society, 74(3), 647 - 665. https://doi.org/10.1080/01605682.2022.2077660 picture_as_pdf
  • Zhang, Junyi, Dassios, Angelos (2023). Truncated Poisson-Dirichlet approximation for Dirichlet process hierarchical models. Statistics and Computing, picture_as_pdf
  • Zhang, Junyi, Dassios, Angelos (2023). Truncated two-parameter Poisson-Dirichlet approximation for Pitman-Yor process hierarchical models. Scandinavian Journal of Statistics, https://doi.org/10.1111/sjos.12688 picture_as_pdf
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  • Feinstein, Zachary, Sojmark, Andreas (2023). Contagious McKean–Vlasov systems with heterogeneous impact and exposure. Finance and Stochastics, 27(3), 663 - 711. https://doi.org/10.1007/s00780-023-00504-2 picture_as_pdf
  • Fryzlewicz, Piotr (2023). Narrowest Significance Pursuit: inference for multiple change-points in linear models. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2023.2211733 picture_as_pdf
  • Maeng, Hyeyoung, Fryzlewicz, Piotr (2023). Detecting linear trend changes in data sequences. Statistical Papers, 16, https://doi.org/10.1007/s00362-023-01458-5 picture_as_pdf
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  • Gao, Yuhe, Shi, Chengchun, Song, Rui (2023). Deep spectral Q-learning with application to mobile health. Stat, 12(1). https://doi.org/10.1002/sta4.564 picture_as_pdf
  • Gavioli-Akilagun, Shakeel (2023). On inference and causality in change point regressions [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004764
  • Ge, Lin, Wang, Jitao, Shi, Chengchun, Wu, Zhenke, Song, Rui (2023). A reinforcement learning framework for dynamic mediation analysis. Proceedings of Machine Learning Research, 202, 11050 - 11097. picture_as_pdf
  • Guo, Shaojun, Qiao, Xinghao (2023). On consistency and sparsity for high-dimensional functional time series with application to autoregressions. Bernoulli, 29(1), 451 - 472. https://doi.org/10.3150/22-BEJ1464 picture_as_pdf
  • Li, Ting, Shi, Chengchun, Wang, Jianing, Zhou, Fan, Zhu, Hongtu (2023). Optimal treatment allocation for efficient policy evaluation in sequential decision making. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (Eds.), Advances in Neural Information Processing Systems 36 (NeurIPS 2023) . Neural Information Processing Systems Foundation. picture_as_pdf
  • Luo, Yu, Graham, Daniel J., McCoy, Emma J. (2023). Semiparametric Bayesian doubly robust causal estimation. Journal of Statistical Planning and Inference, 225, 171 - 187. https://doi.org/10.1016/j.jspi.2022.12.005 picture_as_pdf
  • Wen, Le, Guang, Fengtao, Wang, Yiqing, Sharp, Basil (2023). Decarbonization in New Zealand–where and how: a combination of input–output approach and structural decomposition analysis. New Zealand Economic Papers, https://doi.org/10.1080/00779954.2023.2196676
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  • Hansen, Sakina, Loftus, Joshua (2023). Model-agnostic auditing a lost cause? CEUR Workshop Proceedings, 3442, picture_as_pdf
  • Rosenblatt, Lucas, Herman, Bernease, Holovenko, Anastasia, Lee, Wonkwon, Loftus, Joshua, McKinnie, Elizabeth, Rumezhak, Taras, Stadnik, Andrii, Howe, Bill, Stoyanovich, Julia (2023). Epistemic parity: reproducibility as an evaluation metric for differential privacy. Proceedings of the VLDB Endowment, 16(11), 3178 – 3191. https://doi.org/10.14778/3611479.3611517 picture_as_pdf
  • Suh, Ellie, James, H. (2023). The social, cultural and economic influences on retirement saving for young adults in the UK. In Hofäcker, Dirk, Kuitto, Kati (Eds.), Youth employment insecurity and pension adequacy (pp. 127–145). Edward Elgar. picture_as_pdf
  • Yousefi, Elham, Pronzato, Luc, Hainy, Markus, Müller, Werner G., Wynn, Henry P. (2023). Discrimination between Gaussian process models: active learning and static constructions. Statistical Papers, 64(4), 1275 - 1304. https://doi.org/10.1007/s00362-023-01436-x picture_as_pdf
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  • Jiang, Binyan, Li, Jialiang, Yao, Qiwei (2023). Autoregressive networks. Journal of Machine Learning Research, picture_as_pdf
  • Çetin, Umut, Waelbroeck, Henri (2023). Power laws in market microstructure. In Jarrow, Robert A, Madan, Dilip B (Eds.), Peter Carr Gedenkschrift: Research Advances in Mathematical Finance (pp. 753 - 819). World Scientific (Firm). https://doi.org/10.1142/9789811280306_0022
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  • Kalinke, Florian, Szabo, Zoltan (2023). Nyström M-Hilbert-Schmidt independence criterion. Proceedings of Machine Learning Research, 216, 1005-1015. picture_as_pdf
  • Kotecha, Meena (2023). How can educators prevent the development of mathematics anxiety? Research for the World, picture_as_pdf
  • Kuha, Jouni, Zhang, Siliang, Steele, Fiona (2023). Latent variable models for multivariate dyadic data with zero inflation: analysis of intergenerational exchanges of family support. Annals of Applied Statistics, 17(2), 1521 - 1542. https://doi.org/10.1214/22-AOAS1680 picture_as_pdf
  • Liu, Naijia, Kotecha, Meena (2023-09-04 - 2023-09-07) The relationship between undergraduate students’ mathematics anxiety and motivation to learn mathematics: a mixed method study [Poster]. RSS International Conference 2023, Harrogate Convention Centre, Harrogate, United Kingdom, GBR. picture_as_pdf
  • Vamvourellis, Konstantinos, Kalogeropoulos, Konstantinos, Moustaki, Irini (2023). Assessment of generalised Bayesian structural equation models for continuous and binary data. British Journal of Mathematical and Statistical Psychology, 76(3), 559 - 584. https://doi.org/10.1111/bmsp.12314 picture_as_pdf
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  • Lee, Dabeen, Vojnovic, Milan, Yun, Se-young (2023). Test score algorithms for budgeted stochastic utility maximization. INFORMS Journal on Optimization, 5(1), 27 - 67. https://doi.org/10.1287/ijoo.2022.0075
  • Lillo, Fabrizio, Livieri, Giulia, Marmi, Stefano, Solomko, Anton, Vaienti, Sandro (2023). Analysis of bank leverage via dynamical systems and deep neural networks. SIAM Journal on Financial Mathematics, 14(2), 598 - 643. https://doi.org/10.1137/21M1412517 picture_as_pdf
  • Lillo, Fabrizio, Livieri, Giulia, Marmi, Stefano, Solomko, Anton, Vaienti, Sandro (2023). Unimodal maps perturbed by heteroscedastic noise: an application to a financial systems. Journal of Statistical Physics, 190(10). https://doi.org/10.1007/s10955-023-03160-0 picture_as_pdf
  • Liu, Yirui, Qiao, Xinghao, Wang, Liying, Lam, Jessica (2023). EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model. Proceedings of Machine Learning Research, 206, 2132-2146. picture_as_pdf
  • Loftus, Joshua R. (2023). It’s about time: counterfactual fairness and temporal depth. CEUR Workshop Proceedings, 3442, picture_as_pdf
  • Shi, Chengchun, Wan, Runzhe, Song, Ge, Luo, Shikai, Zhu, Hongtu, Song, Rui (2023). A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets. Annals of Applied Statistics, 17(4), 2701 - 2722. https://doi.org/10.1214/22-AOAS1700 picture_as_pdf
  • Wu, Guojun, Song, Ge, Lv, Xiaoxiang, Luo, Shikai, Shi, Chengchun, Zhu, Hongtu (2023). DNet: distributional network for distributional individualized treatment effects. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, 5215 - 5224. https://doi.org/10.1145/3580305.3599809 picture_as_pdf
  • Xu, Yang, Zhu, Jin, Shi, Chengchun, Luo, Shikai, Song, Rui (2023). An instrumental variable approach to confounded off-policy evaluation. Proceedings of Machine Learning Research, 202, 38848 - 38880. picture_as_pdf
  • Zhang, Xinyu, Li, Dong, Tong, Howell (2023). On the least squares estimation of multiple-threshold-variable autoregressive models. Journal of Business and Economic Statistics, https://doi.org/10.1080/07350015.2023.2174124 picture_as_pdf
  • Zhang, Yingying, Shi, Chengchun, Luo, Shikai (2023). Conformal off-policy prediction. Proceedings of Machine Learning Research, 206, 2751-2768. picture_as_pdf
  • Zhou, Yunzhe, Qi, Zhengling, Shi, Chengchun, Li, Lexin (2023). Optimizing pessimism in dynamic treatment regimes: a Bayesian learning approach. Proceedings of Machine Learning Research, 206, picture_as_pdf
  • Zhou, Yunzhe, Shi, Chengchun, Li, Lexin, Yao, Qiwei (2023). Testing for the Markov property in time series via deep conditional generative learning. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85(4), 1204 - 1222. https://doi.org/10.1093/jrsssb/qkad064 picture_as_pdf
  • Çetin, Umut, Larsen, Kasper (2023). Uniqueness in cauchy problems for diffusive real-valued strict local martingales. Transactions of the American Mathematical Society Series B, 10(13), 381-406. https://doi.org/10.1090/btran/141 picture_as_pdf
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  • Maruri-Aguilar, Hugo, Wynn, Henry (2023). Sparse polynomial prediction. Statistical Papers, 64(4), 1233 - 1249. https://doi.org/10.1007/s00362-023-01439-8 picture_as_pdf
  • Papadimitriou, Dimitris, Tokis, Konstantinos, Vichos, Georgios, Mourdoukoutas, Panos (2023). Managing other people's money: an agency theory in financial management industry. Journal of Financial Research, https://doi.org/10.1111/jfir.12344 picture_as_pdf
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  • Strong, Peter, Shenvi, Aditi, Yu, Xuewen, Papamichail, K. Nadia, Wynn, Henry P., Smith, Jim Q. (2023). Building a Bayesian decision support system for evaluating COVID-19 countermeasure strategies. Journal of the Operational Research Society, 74(2), 476 - 488. https://doi.org/10.1080/01605682.2021.2023673 picture_as_pdf
  • Zhang, Bo, Pan, Guangming, Yao, Qiwei, Wang, Jian-Zhou (2023). Factor modelling for clustering high-dimensional time series. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2023.2183132 picture_as_pdf
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  • Shi, Chengchun, Qi, Zhengling, Wang, Jianing, Zhou, Fan (2023). Value enhancement of reinforcement learning via efficient and robust trust region optimization. Journal of the American Statistical Association, 1-15. https://doi.org/10.1080/01621459.2023.2238942 picture_as_pdf
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  • Sabharwal, Ragvir Singh (2023). On factor models for high-dimensional time series [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004503
  • Wang, Jitao, Shi, Chengchun, Wu, Zhenke (2023). A robust test for the stationarity assumption in sequential decision making. Proceedings of Machine Learning Research, 36355-36379. picture_as_pdf
  • Zhang, Wen, Shi, Jingwen, Wang, Xiaojun, Wynn, Henry (2023). AI-powered decision-making in facilitating insurance claim dispute resolution. Annals of Operations Research, https://doi.org/10.1007/s10479-023-05631-9 picture_as_pdf
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  • Vojnović, Milan, Yun, Se-young, Zhou, Kaifang (2023). Accelerated MM algorithms for inference of ranking scores from comparison data. Operations Research, 71(4), 1318 - 1342. https://doi.org/10.1287/opre.2022.2264
  • Yi, Jialin, Vojnović, Milan (2023). On regret-optimal cooperative nonstochastic multi-armed bandits. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, 2023-M, 1329-1335. https://doi.org/10.5555/3545946.3598780
  • Yi, Jialin, Vojnović, Milan (2023). Doubly adversarial federated bandits. Proceedings of Machine Learning Research, 39951 - 39967. picture_as_pdf
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  • Wallin, Gabriel, Wiberg, Marie (2023). Model misspecification and robustness of observed-score test equating using propensity scores. Journal of Educational and Behavioral Statistics, 48(5), 603 - 635. https://doi.org/10.3102/10769986231161575 picture_as_pdf
  • Çetin, Umut, Waelbroeck, Henri (2023). Power laws in market microstructure. Frontiers of Mathematical Finance, 2(1), 56 - 98. https://doi.org/10.3934/fmf.2023003 picture_as_pdf
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  • Yang, Shuhan (2023). Tools for model selection for mean-nonstationary time series [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004630
  • Yi, Jialin (2023). Regret-minimization algorithms for multi-agent cooperative learning systems [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004523
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  • Zhou, Kaifang (2023). Statistical inference for some choice models [Doctoral thesis]. London School of Economics and Political Science. https://doi.org/10.21953/lse.00004515