Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials
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- Qian Ren & Sudipto Banerjee, 2013. "Hierarchical Factor Models for Large Spatially Misaligned Data: A Low-Rank Predictive Process Approach," Biometrics, The International Biometric Society, vol. 69(1), pages 19-30, March.
- Guhaniyogi, Rajarshi & Banerjee, Sudipto, 2019. "Multivariate spatial meta kriging," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 3-8.
- Harrison Quick & Scott H. Holan & Christopher K. Wikle, 2018. "Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 649-661, June.
- Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
- Eidsvik, Jo & Finley, Andrew O. & Banerjee, Sudipto & Rue, HÃ¥vard, 2012. "Approximate Bayesian inference for large spatial datasets using predictive process models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1362-1380.
- Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
- Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
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