Quefeng Li Department of Biostatistics UNC-Chapel Hill Integrative linear discriminant analysis with guaranteed error rate improvement Numerous empirical studies have found that integrative analysis of multimodal data can result in better statistical performance. However, little theory is known on when and why including more variables in a statistical model can improve the prediction. In the context of two-class classification, we provide a theoretical guarantee that running an integrative linear discriminant analysis on multimodal data achieves smaller misclassification…
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Xi Chen New York University Statistical Inference for Model Parameters with Stochastic Gradient Descent In this talk, we investigate the problem of statistical inference of the true model parameters based on stochastic gradient descent (SGD) with Ruppert-Polyak averaging. To this end, we propose a consistent estimator of the asymptotic covariance of the average iterate from SGD --- batch-means estimator, which only uses the iterates from SGD. As the SGD process forms a time-inhomogeneous Markov chain, our batch-means estimator…
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Tailen Hsing Department of Statistics University of Michigan Modeling and inference of local stationarity Stationarity is a common assumption in spatial statistics. The justification is often that stationarity is a reasonable approximation to the true state of dependence if we focus on spatial data locally. In this talk, we first review various known approaches for modeling nonstationary spatial data. We then examine the notion of local stationarity in more detail. To illustrate, we focus on the multi-fractional Brownian…
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