Calibration of Numerical Model Output Using Nonparametric Spatial Density Functions
نویسندگان
چکیده
منابع مشابه
Calibration of numerical model output using nonparametric spatial density functions
The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of mean and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for assessment of model p...
متن کاملComputer Model Calibration using High Dimensional Output
This work focuses on combining observations from field experiments with detailed computer simulations of a physical process to carry out statistical inference. Of particular interest here is determining uncertainty in resulting predictions. This typically involves calibration of parameters in the computer simulator as well as accounting for inadequate physics in the simulator. We consider appli...
متن کاملComputer Model Calibration with Multivariate Spatial Output: A Case Study
Computer model calibration involves combining information from simulations of a complex computer model with physical observations of the process being simulated by the model. Increasingly, computer model output is in the form of multiple spatial fields, particularly in climate science. We study a simple and effective approach for computer model calibration with multivariate spatial data. We dem...
متن کاملA proposed nonparametric mixture density estimation using B-spline functions
In this paper, we suppose that a density of probability f is expressed as a finite linear combination of second order B-spline functions. Then, we obtain a finite mixture of B-spline. We extend the Expectation Maximization (EM) algorithm in order to estimate the new mixture density. The experiments show that the proposed estimator using B-spline functions can produce a satisfactory estimation o...
متن کاملStatistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm
This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean square error (IMSE). The results of simulation studies show a significant impro...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Agricultural, Biological, and Environmental Statistics
سال: 2011
ISSN: 1085-7117,1537-2693
DOI: 10.1007/s13253-011-0076-4