Statistics Seminars, 17 Mar 2017 19:02:54 +0000Lei Sun - Testing a vector of parameters with applications to genetic association studies seminar:<br> Lei Sun, University of Toronto, Department of Statistical Sciences<br> &ldquo;Testing a vector of parameters with applications to genetic association studies&rdquo;<br> Thursday, March 30, 2017 at 2:45 p.m.<br> 527 Buller<br> <br> <p>In many scientific studies, a vector of location and/or scale parameters may be of inferential interest. For example, genetic association studies between an outcome and multiple genetic variants (also known as gene-based or set-based association analyses) simultaneously investigate a vector of location parameters. In another setting where heteroscedasticity may be present due to unaccounted for interaction effects, joint analyses of both location and scale parameters can be more powerful. In the context of gene-based association analyses, we show that many existing methods can be classified into a class of linear statistics and another class of quadratic statistics, where each class is powerful only in part of the high-dimensional parameter space (Derkach et al. 2014, Statistical Science). Consequently we can derive a more robust class of hybrid test statistics, by combining evidence from the competing but complementary individual linear and quadratic test statistics. Similarly, we develop a joint location-scale testing framework to test the global null of no mean and no variance heterogeneity (Soave et al. 2015, the American Journal of Human Genetics; Soave and Sun, in press, Biometrics). We apply Fisher’s method, commonly used in meta-analyses to combine p-values of the same test applied to different samples, to combine p-values of different tests (i.e. linear and quadratic, or location and scale) applied to the same sample. In both settings, we show that the two classes of tests are asymptotically independent of each other under the global null hypothesis. Thus, we can evaluate the significance of the resulting Fisher’s test statistic using the chi-squared distribution with four degrees of freedom; this is a desirable feature for analyzing big data. In addition to theoretical results, we also provide empirical results from extensive simulation studies and multiple data applications.</p> Fri, 17 Mar 2017 19:02:46 +0000 Huang - Restoration of Monotonicity Respecting in Dynamic Regression seminar:<br> Eugene Huang, Emory University, Department of Biostatistics and Bioinformatics<br> &ldquo;Restoration of Monotonicity Respecting in Dynamic Regression&rdquo;<br> Thursday, April 6, 2017 at 2:45 p.m.<br> 527 Buller<br> <br> <p>Dynamic regression models, including the quantile regression model and Aalen's additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this talk, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and then interpolating nearest monotonicity-respecting points of an original estimator. Under mild regularity conditions, the resulting regression coefficient estimator is shown to be asymptotically equivalent to the original. Our numerical studies have demonstrated that the proposed estimator is much more smooth and may have better finite-sample efficiency than the original as well as, when available as only in special cases, other competing monotonicity-respecting estimators. Illustration with a clinical study is provided.</p> Fri, 17 Mar 2017 19:02:54 +0000