[图书][B] Resampling methods for dependent data
SN Lahiri - 2013 - books.google.com
This is a book on bootstrap and related resampling methods for temporal and spatial data
exhibiting various forms of dependence. Like the resam pling methods for independent data …
exhibiting various forms of dependence. Like the resam pling methods for independent data …
Design of computer experiments: space filling and beyond
L Pronzato, WG Müller - Statistics and Computing, 2012 - Springer
When setting up a computer experiment, it has become a standard practice to select the
inputs spread out uniformly across the available space. These so-called space-filling …
inputs spread out uniformly across the available space. These so-called space-filling …
Surrogate model uncertainty quantification for reliability-based design optimization
Surrogate models have been widely employed as approximations of expensive physics-
based simulations to alleviate the computational burden in reliability-based design …
based simulations to alleviate the computational burden in reliability-based design …
Methods to compute prediction intervals: A review and new results
Methods to Compute Prediction Intervals: A Review and New Results Page 1 Statistical Science
2022, Vol. 37, No. 4, 580–597 https://doi.org/10.1214/21-STS842 © Institute of Mathematical …
2022, Vol. 37, No. 4, 580–597 https://doi.org/10.1214/21-STS842 © Institute of Mathematical …
On parametric bootstrap methods for small area prediction
P Hall, T Maiti - Journal of the Royal Statistical Society Series B …, 2006 - academic.oup.com
The particularly wide range of applications of small area prediction, eg in policy making
decisions, has meant that this topic has received substantial attention in recent years. The …
decisions, has meant that this topic has received substantial attention in recent years. The …
Confidence-based design optimization for a more conservative optimum under surrogate model uncertainty caused by Gaussian process
Y Jung, K Kang, H Cho, I Lee - Journal of …, 2021 - asmedigitalcollection.asme.org
Even though many efforts have been devoted to effective strategies to build accurate
surrogate models, surrogate model uncertainty is inevitable due to a limited number of …
surrogate models, surrogate model uncertainty is inevitable due to a limited number of …
The correct Kriging variance estimated by bootstrapping
D Den Hertog, JPC Kleijnen… - Journal of the Operational …, 2006 - Taylor & Francis
The classic Kriging variance formula is widely used in geostatistics and in the design and
analysis of computer experiments. This paper proves that this formula is wrong. Furthermore …
analysis of computer experiments. This paper proves that this formula is wrong. Furthermore …
Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations
Lidar provides critical information on the three-dimensional structure of forests. However,
collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As …
collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As …
Fast kriging of large data sets with Gaussian Markov random fields
L Hartman, O Hössjer - Computational Statistics & Data Analysis, 2008 - Elsevier
Spatial data sets are analysed in many scientific disciplines. Kriging, ie minimum mean
squared error linear prediction, is probably the most widely used method of spatial …
squared error linear prediction, is probably the most widely used method of spatial …
Classical geostatistical methods
Suppose that a spatially distributed variable is of interest, which in theory is defined at every
point over a bounded study region of interest, D⊂ Rd, whered= 2 or 3. We suppose further …
point over a bounded study region of interest, D⊂ Rd, whered= 2 or 3. We suppose further …