Statistics Across Campuses

   Far and Close: Selective Sample Enrichment to Deal with Multicollinearity in Geographically Weighted Regression Date :  Friday, 30 July 2021 Time:   12-1pm Speaker:  Dr P atricia Menendez  ( Department of Econometrics and Business Statistics at Monash University ) Abstract: Geographically weighted regression (GWR) is a popular technique to deal with spatially varying relationships between a response variable and a set of predictors. However, GWR estimates might be affected by multicollinearity issues related to locally poor designs. In this study, we propose two regularization methods to deal with those issues. The first one is based on a generalized ridge regression, which can also be seen as an empirical Bayes method. We show that it can be implemented using ordinary GWR software with an appropriate choice of the weights. The second one augments the local sample using an enrichment strategy. The methods will be illustrated with simulations and with an example of housing prices in

   Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric errors Date :  Wednesday, 28 July 2021 Time:   3-4 pm Speaker:  Associate Professor Yuanyuan LIN  (Chinese University of Hong Kong) Abstract: We propose a robust post-selection inference method based on the Huber loss for the regression coefficients, when the error distribution is heavy-tailed and asymmetric in a high-dimensional linear model with an intercept term. The asymptotic properties of the resulting estimators are established under mild conditions. We also extend the proposed method to accommodate heteroscedasticity assuming the error terms are symmetric and other suitable conditions. Statistical tests for low-dimensional parameters or individual coefficient in the high-dimensional linear model are also studied. Simulation studies demonstrate desirable properties of the proposed method. An application to a genomic dataset about riboflavin production rate is provided Zoom Link: 

Semi-Supervised Learning of a Classifier from a Statistical Perspective Date: 23 July 2021, Friday Time: 4pm AEDT Speaker: Prof Geoffrey McLachlan (University fo Queensland) Abstract: With the considerable interest on machine learning these days, there is increasing attention being given to a semi-supervised learning (SSL) approach to constructing a classifier. From a statistical perspective, it goes back over 50 years (McLachlan, 1975, JASA). As is well known, the (Fisher) information in an unclassified feature with unknown class label is less (considerably less for weakly separated classes) than that of a classified feature which has known class label. Hence in the case where the absence of class labels does not depend on the data, the expected error rate of a classifier formed from the classified and unclassified features in a partially classified sample can be relatively much greater than that if the sample were completely classified (Ganesalingam and McLachlan, 1978, Biomet