Browsing by Author "Montserrat Fuentes, Co-Chair"
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- Bayesian Quantile Regression in Biostatistical Applications.(2014-04-29) Smith, Luke Brawley; Montserrat Fuentes, Co-Chair; Brian Reich, Co-Chair; Yichao Wu, Member; Yang Zhang, Member
- A Bayesian Spatial Analysis of Extreme Precipitation.(2010-12-06) Alston, Shenek; Montserrat Fuentes, Co-Chair; Lian Xie, Co-Chair; David Dickey, Member; Brian Reich, Member; Donald Martin, Member
- Bridge Models and Variable Selection Methods for Spatial Data.(2013-05-09) Boehm, Laura Frances; Brian Reich, Co-Chair; Montserrat Fuentes, Co-Chair; Yichao Wu, Member; Henry Frey, Member; Ana-Maria Staicu, Member
- Computationally Efficient Estimation of Non-stationary Gaussian Process Models for Large Spatial Data.(2019-02-15) Muyskens, Amanda Leigh; Montserrat Fuentes, Co-Chair; Joseph Guinness, Co-Chair; Soumendra Lahiri, Member; Dean Hesterberg, Member
- Nonparametric Spatial analysis in spectral and space domains(2000-08-23) Kim, Hyon-Jung; Dennis D. Boos, Chair; Montserrat Fuentes, Co-Chair; Bibhuti B. Bhattacharyya, Member; Marcia L. Gumpertz, Member; Jerry M. Davis, MemberThe empirical semivariogram of residuals from a regression model withstationary errors may be used to estimate the covariance structure of the underlyingprocess.For prediction (Kriging) the bias of the semivariogram estimate induced byusing residuals instead of errors has only a minor effect because thebias is small for small lags. However, for estimating the variance of estimatedregression coefficients and of predictions,the bias due to using residuals can be quite substantial. Thus wepropose a method for reducing the bias in empirical semivariogram estimatesbased on residuals. The adjusted empirical semivariogram is then isotonizedand made positive definite and used to estimate the variance of estimatedregression coefficients in a general estimating equations setup.Simulation results for least squares and robust regression show that theproposed method works well in linear models withstationary correlated errors. Spectral Analysis with Spatial Periodogram and Data Tapers.(Under the direction of Professor Montserrat Fuentes.)The spatial periodogram is a nonparametric estimate of the spectral density, which is the Fourier Transform of the covariance function. The periodogram is a useful tool to explain the dependence structure of aspatial process.Tapering (data filtering) is an effective technique to remove the edge effects even inhigh dimensional problemsand can be applied to the spatial data in order to reduce the bias of the periodogram.However, the variance of the periodogram increases as the bias is reduced.We present a method to choose an appropriate smoothing parameter for datatapers and obtain better estimates of the spectral densityby improving the properties of the periodogram.The smoothing parameter is selected taking intoaccount the trade-off between bias and variance of the taperedperiodogram. We introduce a new asymptotic approach for spatial datacalled `shrinking asymptotics', which combines theincreasing-domain and the fixed-domain asymptotics.With this approach, the tapered spatial periodogram can be usedto determine uniquely the spectral density of the stationary process,avoiding the aliasing problem.
- Quantile Regression and Hierarchical Models for Near-source Air Quality Data.(2019-05-09) Brantley, Halley Lee; Montserrat Fuentes, Co-Chair; Joseph Guinness, Co-Chair; Brian Reich, Member; Eric Chi, Member; Henry Frey, Member
- Spatial Signal Detection Using Continuous Shrinkage Priors.(2018-04-12) Jhuang, An-Ting; Montserrat Fuentes, Co-Chair; Brian Reich, Co-Chair; Ana-Maria Staicu, Member; Luo Xiao, Member; Karen Leonas, Graduate School Representative
- Variable Selection Methods with Applications to Atmospheric Sciences.(2017-05-02) Alfaro Cordoba, Marcela; Montserrat Fuentes, Co-Chair; Joseph Guinness, Co-Chair; Ana-Maria Staicu, Member; Lian Xie, Member
