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Abstract:
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A high dimensional setting when the number of subjects is substantially smaller than the number of conditions to be tested is widely encountered in a variety of modern longitudinal and repeated measures design studies, with applications ranging from medicine to social sciences. Recently, there have been suggested several global testing procedures for high-dimensional repeated measures designs that can be employed to assess the global null hypothesis, e.g. of no global time effect. In statistical practice, however, frequently the key question of interest is identification of the significant factor levels, along the computation of simultaneous confidence intervals for treatment effects. In this talk we consider two approaches, namely, regularization and resampling, that can be employed to derive multiple contrast tests and simultaneous confidence intervals in a high dimensional setting. We discuss asymptotic properties of the proposed testing procedures and illustrate their finite-sample performance by simulations and case studies.
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