scholarly journals The intrinsic random functions and their applications

1973 ◽  
Vol 5 (3) ◽  
pp. 439-468 ◽  
Author(s):  
G. Matheron

The intrinsic random functions (IRF) are a particular case of the Guelfand generalized processes with stationary increments. They constitute a much wider class than the stationary RF, and are used in practical applications for representing non-stationary phenomena. The most important topics are: existence of a generalized covariance (GC) for which statistical inference is possible from a unique realization; theory of the best linear intrinsic estimator (BLIE) used for contouring and estimating problems; the turning bands method for simulating IRF; and the models with polynomial GC, for which statistical inference may be performed by automatic procedures.

1973 ◽  
Vol 5 (03) ◽  
pp. 439-468 ◽  
Author(s):  
G. Matheron

The intrinsic random functions (IRF) are a particular case of the Guelfand generalized processes with stationary increments. They constitute a much wider class than the stationary RF, and are used in practical applications for representing non-stationary phenomena. The most important topics are: existence of a generalized covariance (GC) for which statistical inference is possible from a unique realization; theory of the best linear intrinsic estimator (BLIE) used for contouring and estimating problems; the turning bands method for simulating IRF; and the models with polynomial GC, for which statistical inference may be performed by automatic procedures.


Author(s):  
Cédric Rommel ◽  
Joseph Frédéric Bonnans ◽  
Baptiste Gregorutti ◽  
Pierre Martinon

In this paper, we tackle the problem of quantifying the closeness of a newly observed curve to a given sample of random functions, supposed to have been sampled from the same distribution. We define a probabilistic criterion for such a purpose, based on the marginal density functions of an underlying random process. For practical applications, a class of estimators based on the aggregation of multivariate density estimators is introduced and proved to be consistent. We illustrate the effectiveness of our estimators, as well as the practical usefulness of the proposed criterion, by applying our method to a dataset of real aircraft trajectories.


2016 ◽  
Vol 108 ◽  
pp. 33-39 ◽  
Author(s):  
Chunfeng Huang ◽  
Haimeng Zhang ◽  
Scott M. Robeson

1986 ◽  
Vol 22 (6) ◽  
pp. 935-942 ◽  
Author(s):  
E. Feinerman ◽  
G. Dagan ◽  
E. Bresler

1988 ◽  
Vol 20 (6) ◽  
pp. 699-715 ◽  
Author(s):  
Katherine Campbell

2019 ◽  
Vol 146 ◽  
pp. 7-14 ◽  
Author(s):  
Chunfeng Huang ◽  
Haimeng Zhang ◽  
Scott M. Robeson ◽  
Jacob Shields

Bernoulli ◽  
2013 ◽  
Vol 19 (2) ◽  
pp. 387-408 ◽  
Author(s):  
Michael L. Stein

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