Fetching the paper…
Reading the bibliography…
In spatial statistics, a common objective is to predict values of a spatial process at unobserved locations by exploiting spatial dependence.
Variational inference to measure model uncertainty in deep neural networks
Posch, K., Steinbrener, J., and Pilz, J. (2019) · 1902
Earlier work this paper cites.
Li, R., Bondell, H. D., and Reich, B. J. (2019) · 1903
Earlier work this paper cites.
A selective overview of deep learning
Fan, J., Ma, C., and Zhong, Y. (2019) · 1904
Earlier work this paper cites.
Deep compositional spatial models
Zammit-Mangion, A., Ng, T. L. J., Vu, Q., and Filippone, M. (2019) · 1906
Earlier work this paper cites.
On stationary processes in the plane
Whittle, P. (1954) · 1954
Earlier work this paper cites.
Principles of geostatistics
Matheron, G. (1963) · 1963
Earlier work this paper cites.
Generalized inverse of matrices and its applications
Banerjee, K. S. (1973) · 1973
Earlier work this paper cites.
Some aspects of wind power statistics
Hennessey Jr, J. P. (1977) · 1977
Earlier work this paper cites.
Splines and kriging: their formal equivalence
Matheron, G. (1981) · 1981
Earlier work this paper cites.
On the histogram as a density estimator: L2 theory
Freedman, D. and Diaconis, P. (1981) · 1981
Earlier work this paper cites.
Two methods with different objectives: splines and kriging
Dubrule, O. (1983) · 1983
Earlier work this paper cites.
Comparing splines and kriging
Dubrule, O. (1984) · 1984
Earlier work this paper cites.
The origins of kriging
Cressie, N. (1990) · 1990
Earlier work this paper cites.
Spline Models for Observational Data
Wahba, G. (1990) · 1990
Earlier work this paper cites.
Priors for infinite networks
Neal, R. M. (1994) · 1994
Earlier work this paper cites.
Priors for infinite networks
Neal, R. M. (1994) · 1994
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M. (1996) · 1996
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M. (1996) · 1996
Earlier work this paper cites.
Spatial econometrics
Anselin, L. (2001) · 2001
Earlier work this paper cites.
Approximation with artificial neural networks
Csáji, B. C. (2001) · 2001
Earlier work this paper cites.
The Elements of Statistical Learning
Friedman, J., Hastie, T., and Tibshirani, R. (2001) · 2001
Earlier work this paper cites.
Increased particulate air pollution and the triggering of myocardial infarction
Peters, A., Dockery, D. W., Muller, J. E., and Mittleman, M. A. (2001) · 2001
Earlier work this paper cites.
Approximation with artificial neural networks
Csáji, B. C. (2001) · 2001
Earlier work this paper cites.
Spatial and temporal variability in outdoor, indoor, and personal PM2.5 exposure
Adgate, J. L., Ramachandran, G., Pratt, G., Waller, L., and Sexton, K. (2002) · 2002
Earlier work this paper cites.
Spatial prediction of species distribution: an interface between ecological theory and statistical modeling
Austin, M. (2002) · 2002
Earlier work this paper cites.
Spectral methods for nonstationary spatial processes
Fuentes, M. (2002) · 2002
Cited alongside, same era.
Space and space-time modeling using process convolutions
Higdon, D. (2002) · 2002
Cited alongside, same era.
Nonstationary covariance functions for Gaussian process regression
Paciorek, C. J. and Schervish, M. J. (2004) · 2004
Cited alongside, same era.
Applied Spatial Statistics for Public Health Data
Waller, L. A. and Gotway, C. A. (2004) · 2004
Cited alongside, same era.
Optimal Statistical Decisions
DeGroot, M. H. (2005) · 2005
Cited alongside, same era.
Theory of point estimation
Lehmann, E. L. and Casella, G. (2006) · 2006
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Later among the works it cites.
Statistics for Spatial Data
Cressie, N. (2015) · 2015
Later among the works it cites.
Equivalent kriging
Kleiber, W. and Nychka, D. W. (2015) · 2015
Later among the works it cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
Later among the works it cites.
Deep learning applications and challenges in big data analytics
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., and Muharemagic, E. (2015) · 2015
Later among the works it cites.
A multiresolution Gaussian process model for the analysis of large spatial datasets
Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F., and Sain, S. (2015) · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diggle, P. J., Thomson, M. C., Christensen, O., Rowlingson, B., Obsomer, V., Gardon, J., Wanji, S., Takougang, I., Enyong, P., Kamgno, J., et al. (2007) · 2007
Cited alongside, same era.
Gaussian predictive process models for large spatial data sets
Banerjee, S., Gelfand, A. E., Finley, A. O., and Sang, H. (2008) · 2008
Cited alongside, same era.
Fixed rank kriging for very large spatial data sets
Cressie, N. and Johannesson, G. (2008) · 2008
Cited alongside, same era.
Kernel methods for deep learning
Cho, Y. and Saul, L. K. (2009) · 2009
Cited alongside, same era.
Effect of climate change on air quality
Jacob, D. J. and Winner, D. A. (2009) · 2009
Cited alongside, same era.
Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution
Peng, R. D., Bell, M. L., Geyh, A. S., McDermott, A., Zeger, S. L., Samet, J. M., and Dominici, F. (2009) · 2009
Cited alongside, same era.
Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression
Porter, W. C., Heald, C. L., Cooley, D., and Russell, B. (2015) · 2015
Later among the works it cites.
Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F., and Sain, S. (2015). A multi-resolution Gaussian process model for the analysis of large spatial datasets. Journal of Computational and Graphical Statistics,
2015
Later among the works it cites.
Wide & deep learning for recommender systems
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al. (2016) · 2016
Later among the works it cites.
Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., and Schwartz, J. (2016) · 2016
Later among the works it cites.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
Later among the works it cites.
Improving ecological inference by predicting individual ethnicity from voter registration records
Imai, K. and Khanna, K. (2016) · 2016
Later among the works it cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
Later among the works it cites.
A multi-resolution approximation for massive spatial datasets
Katzfuss, M. (2017) · 2017
Later among the works it cites.
Tukey g-and-h random fields
Xu, G. and Genton, M. G. (2017) · 2017
Later among the works it cites.
Deep Learning with Python
Ketkar, N. et al. (2017) · 2017
Later among the works it cites.
Deep neural networks as gaussian processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J. (2017) · 2017
Later among the works it cites.
Supervised deep kriging for single-image super-resolution
Franchi, G., Yao, A., and Kolb, A. (2018) · 2018
Later among the works it cites.
Deep neural networks as Gaussian processes
Lee, J., Sohl-Dickstein, J., Pennington, J., Novak, R., Schoenholz, S., and Bahri, Y. (2018) · 2018
Later among the works it cites.
Deep gaussian processes with convolutional kernels
Kumar, V., Singh, V., Srijith, P., and Damianou, A. (2018) · 2018
Later among the works it cites.
National ambient air quality standards (NAAQS)
EPA, U. (2012) · 2019
Later among the works it cites.
A case study competition among methods for analyzing large spatial data
Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., et al. (2019) · 2019
Later among the works it cites.
Efficient estimation of non-stationary spatial covariance functions with application to high-resolution climate model emulation
Li, Y. and Sun, Y. (2019) · 2019
Later among the works it cites.
Deep convolutional gaussian processes
Blomqvist, K., Kaski, S., and Heinonen, M. (2019) · 2019
Later among the works it cites.
Machine learning predictions as regression covariates
Fong, C. and Tyler, M. (2021) · 2021
Closest in time.