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B0398
Title: On the estimation of ultra-high dimensional semiparametric Gaussian copula models Authors:  Qing Mai - Florida State University (United States) [presenting]
Abstract: The semiparametric Gaussian copula model has wide applications in econometrics, finance and statistics. Recently, many have considered applications of semiparametric Gaussian copula model in several high-dimensional learning problems. We propose a slightly modified normal score estimator and a new Winsorized estimator for estimating both nonparametric transformation functions and the correlation matrix of the semiparametric Gaussian copula model. Two new concentration inequalities are derived, based on which we show that the normal score estimator and the new Winsorized estimator are consistent when the dimension grows at an exponential rate of the sample size. As demonstration, we apply our theory to two high-dimensional learning problems: semiparametric Gaussian graphical model and semiparametric discriminant analysis.