Research Interests

Statistical theory; Machine learning; Biostatistics


Working Papers

Travis-Lumer, Y. and Goldberg, Y. Support Vector Machines for Current Status Data. The code can be found here.

Refereed Journals

Goldberg, Y., and Kosorok, K. In press. Support vector regression for right censored data. Electronic Journal of Statistics.

Gorfine, M., Goldberg, Y., and Ritov, Y. (2017). A quantile regression model for failure-time data with time-dependent covariates. Biostatistics 18, pages 132-146.

Vakulenko-Lagun, B., Mandel, M., and Goldberg, Y. (2017). Nonparametric estimation in the illness-death model using prevalent data. Lifetime Data Analysis 23, pages 25-56.

Goldberg, Y., Lu, W., and Fine, J. (2016) Oracle estimation of parametric transformation models. Electronic Journal of Statistics 10, pages 90-120.

Wong, K. Y., Goldberg, Y., and Fine, J. P. (2016). Oracle estimation of parametric models under boundary constraints. Biometrics 72, pages 1173-1183.

Levine, S. Z., Kodesh, A., Goldberg, Y., Reichenberg, A., Furukawa, T. A., Kolevzon, A., and Leucht, S. (2016). Initial severity and efficacy of risperidone in autism: Results from the RUPP trial. European Psychiatry 32, pages 16-20.

Levine, S. Z., I. Levav, Y. Goldberg, I. Pugachova, Y. Becher, and R. Yoffe. (2016). Exposure to genocide and the risk of schizophrenia: a population-based study. Psychological Medicine 46, pages 855-863.

Vadasz, Z., Goldeberg, Y., Halasz, K., Rosner, I., Valesini, G., Conti, F., Perricone, C., Sthoeger, Z., Bezalel, S.R., Tzioufas, A.G. and Levin, N.A., (2016). Increased soluble CD72 in systemic lupus erythematosus is in association with disease activity and lupus nephritis. Clinical Immunology 164, pages 114-118.

Vancak, V., Goldberg, Y., Bar-Lev, S., and Boukai, B., Continuous Statistical Models: With or Without Truncation Parameters? (2015) Mathematical Methods of Statistics 24, pages 55-73.

Levine, S.Z., Goldberg, Y., Samara, M., Davis, J.M. and Leucht, S., (2015). Joint modeling of dropout and outcome in three pivotal clinical trials of schizophrenia. Schizophrenia Research 164, pages 122-126.

Furukawa, T.A., Levine, S.Z., Tanaka, S., Goldberg, Y., Samara, M., Davis, J.M., Cipriani, A. and Leucht, S., (2015). Initial severity of schizophrenia and efficacy of antipsychotics: participant-level meta-analysis of 6 placebo-controlled studies. JAMA Psychiatry 72, pages 14-21.

Goldberg, Y. and Nov, Y. (2015). Modeling and Optimization of Gene Detection Experiments. Probability in the Engineering and Informational Sciences 29, pages 131-145.

Levine, S.Z., Goldberg, Y., Yoffe, R., Pugachova, I. and Reichenberg, A., (2014). Suicide attempts in a national population of twins concordant for psychoses. European Neuropsychopharmacology 24, pages 1203-1209.

Goldberg, Y., Song, R., Zeng, D, Kosorok, M. R., (2014) Comment on “Dynamic treatment regimes: Technical challenges and applications”, Electronic Journal of Statistics 8, pages 1290-1300.

Goldberg, Y., Ritov, Y. and Mandelbaum, A. (2014). Predicting the Continuation of a Function with Applications to Call Center Data. Journal of Statistical Planning and Inference 147, pages 53-65.

Goldberg, Y., Song, R. and Kosorok, M. R. (2013). Adaptive Q-learning. In: From Probability to Statistics and Back: High-Dimensional Models and Processes -- A Festschrift in Honor of Jon Wellner. Eds. M. Banerjee, F. Bunea, J. Huang, V. Koltchinskii, M. H. Maathuis. Collections, Volume 9. Institute of Mathematical Statistics, pages 150-162.

Goldberg, Y. and Kosorok, M. R. (2012). Q-learning with censored data. The Annals of Statistics 40, pages 529–560.

Goldberg, Y. and Ritov, Y. (2012). Theoretical analysis of LLE based on its weighting step. Journal of Computational and Graphical Statistics 21, pages 380–393.

Lu, W., Goldberg, Y. and Fine, J. P. (2012). On the robustness of the adaptive lasso to model misspecification. Biometrika 99, pages 717–731.

Goldberg, Y. and Kosorok, M. R. (2012). An exponential bound for cox regression. Statistics & Probability Letters 82, pages 1267–1272.

Goldberg, Y. and Kosorok, M. R. (2011). Comment on “Adaptive confidence intervals for the test error in classification” by E. B. Labor and S. A. Murphy. Journal of the American Statistical Association 106, pages 920–924.

Goldberg, Y. and Ritov, Y. (2009). Local Procrustes for manifold embedding: a measure of embedding quality and embedding algorithms. Machine Learning 77, pages 1–25.

Goldberg, Y., Zakai, A., Kushnir, D. and Ritov, Y. (2008). Manifold learning: The price of normalization. Journal of Machine Learning Research 9, pages 1909–1939.

Goldberg, Y. (2007). Secret correlation in repeated games with imperfect monitoring: The need for nonstationary strategies. Mathematics of Operations Research 32, pages 425–435.

Refereed Conference Proceedings

Goldberg Y., and Ritov Y. (2008) LDR-LLE: LLE with low-dimensional neighborhood representation. 4th International Symposium on Visual Computing (ISVC08) 43–54.

Ph.D. dissertation

Goldberg Y. On dimension reduction using manifolds. Hebrew University, 2009.