/tags/2018-fall/index.xml 2018 Fall - McGill Statistics Seminars

2018 Fall

  • Quantile LASSO in Nonparametric Models with Changepoints Under Optional Shape Constraints

    Matus Maciak · Sep 14, 2018

    Date: 2018-09-14

    Time: 15:30-16:30

    Location: BURN 1104

    Abstract:

    Nonparametric models are popular modeling tools because of their natural overall flexibility. In our approach, we apply nonparametric techniques for panel data structures with changepoints and optional shape constraints and the estimation is performed in a fully data driven manner by utilizing atomic pursuit methods – LASSO regularization techniques in particular. However, in order to obtain robust estimates and, also, to have a more complex insight into the underlying data structure, we target conditional quantiles rather then the conditional mean only. The whole estimation process and the following inference become both more challenging but the results are more useful in practical applications. The underlying model is firstly introduced and some theoretical results are presented. The proposed methodology is applied for a real data scenario and some finite sample properties are investigated via an extensive simulation study. This is a joint work with Ivan Mizera, University of Alberta and Gabriela Ciuperca, University of Lyon

  • Association Measures for Clustered Competing Risks Data

    Chien-Lin (Mark) Su · Sep 7, 2018

    Date: 2018-09-07

    Time: 15:30-16:30

    Location: BURN 1104

    Abstract:

    In this work, we propose a semiparametric model for multivariate clustered competing risks data when the cause-specific failure times and the occurrence of competing risk events among subjects within the same cluster are of interest. The cause-specific hazard functions are assumed to follow Cox proportional hazard models, and the associations between failure times given the same or different cause events and the associations between occurrences of competing risk events within the same cluster are investigated through copula models. A cross-odds ratio measure is explored under our proposed models. Two-stage estimation procedure is proposed in which the marginal models are estimated in the first stage, and the dependence parameters are estimated via an Expectation-Maximization algorithm in the second stage. The proposed estimators are shown to yield consistent and asymptotically normal under mild regularity conditions. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to a multicenter Bone Marrow transplantation dataset.