Global estimation variance: Formulas and calculation

1985 ◽  
Vol 17 (8) ◽  
pp. 785-796 ◽  
Author(s):  
D. Crozel ◽  
M. David
Keyword(s):  
Estimation Variance ◽  
Global Estimation ◽  
Global Estimation Variance

scholarly journals PERBANDINGAN TIGA PENDEKATAN GEOSTATISTIK UNTUK MEMODELKAN KETIDAKPASTIAN DALAM ESTIMASI SUMBERDAYA TIMAH DAN MINERAL IKUTAN TIMAH PADA ENDAPAN ALUVIAL

2021 ◽  
Vol 2 (2) ◽  
pp. 65-74
Author(s):  
Raymond Kosher Sianturi ◽  
Mohamad Nur Heriawan ◽  
Syafrizal Syafrizal ◽  
Cahyo Okta Ardian ◽  
Satyogroho Dian Amertho ◽  
...  
Keyword(s):  
Gaussian Model ◽  
Sequential Gaussian Simulation ◽  
Change Of Support ◽  
Estimation Variance ◽  
Discrete Gaussian Model ◽  
Global Estimation ◽  
Global Estimation Variance ◽  
Gaussian Simulation

Blok C merupakan salah satu blok endapan aluvial di Pulau Bangka yang memiliki prospek timah dan mineral ikutan timah seperti ilmenite, rutile, anatase, zircon, dan monazite. Endapan aluvial umumnya memiliki variabilitas yang tinggi sehingga faktor ketidakpastian akan sumberdaya timah dan mineral ikutan timah juga tinggi. Pada penelitian ini dilakukan perbandingan antara 3 (tiga) pendekatan geostatistik untuk memodelkan ketidakpastian sumberdaya dengan studi kasus pada endapan aluvial di Blok C di Pulau Bangka. Untuk mengetahui variabilitas global di daerah penelitian dilakukan dengan menggunakan metode Global Estimation Variance (GEV), sedangkan untuk mengetahui variabilitas lokal dilakukan menggunakan Sequential Gaussian Simulation (SGS) dan Discrete Gaussian Model (DGM). Hasil dari metode GEV dibandingkan dengan metode SGS dan hasil dari metode SGS juga akan dibandingkan dengan metode DGM. Dari hasil perbandingan GEV dan SGS menunjukkan bahwa hasil GEV cenderung less confidence jika dibandingkan dengan hasil SGS. Less confidence pada hasil GEV disebabkan oleh efek proporsional di daerah penelitian. Hasil perbandingan SGS dan DGM menunjukkan pola yang hampir sama untuk Sn (timah) dan ilmenite+rutile+anatase serta pola yang cukup berbeda untuk zircon. Perbedaan ini disebabkan oleh pemusatan data yang merupakan bagian dari metode DGM. Selain itu, mayoritas nilai minimum hasil DGM lebih besar daripada nilai minimum hasil SGS dan nilai maksimum hasil DGM lebih kecil daripada nilai maksimum hasil SGS. Hal ini disebabkan oleh change of support coefficient (r) yang mempengaruhi fungsi dari transformasi

scholarly journals A Bayesian approach to the global estimation of maternal mortality

2017 ◽  
Vol 11 (3) ◽  
pp. 1245-1274 ◽  
Author(s):  
Leontine Alkema ◽  
Sanqian Zhang ◽  
Doris Chou ◽  
Alison Gemmill ◽  
Ann-Beth Moller ◽  
...  
Keyword(s):  
Maternal Mortality ◽  
Bayesian Approach ◽  
Global Estimation

Cokriging

2004 ◽  
Author(s):  
Vera Pawlowsky-Glahn ◽  
Richardo A. Olea
Keyword(s):  
Normal Distribution ◽  
Multivariate Normal Distribution ◽  
Real Space ◽  
Sample Space ◽  
Multivariate Normal ◽  
Estimation Variance ◽  
Matrix Formulation ◽  
Simple Kriging ◽  
Usual Procedure ◽  
The Face

The problem of estimation of a coregionalization of size q using cokriging will be discussed in this chapter. Cokriging—a multivariate extension of kriging—is the usual procedure applied to multivariate regionalized problems within the framework of geostatistics. Its fundament is a distribution-free, linear, unbiased estimator with minimum estimation variance, although the absence of constraints on the estimator is an implicit assumption that the multidimensional real space is the sample space of the variables under consideration. If a multivariate normal distribution can be assumed for the vector random function, then the simple kriging estimator is identical with the conditional expectation, given a sample of size N. See Journel (1977, pp. 576-577), Journel (1980, pp. 288-290), Cressie (1991, p. 110), and Diggle, Tawn, and Moyeed (1998, p. 300) for further details. This estimator is in general the best possible linear estimator, as it is unbiased and has minimum estimation variance, but it is not very robust in the face of strong departures from normality. Therefore, for the estimation of regionalized compositions other distributions must also be taken into consideration. Recall that compositions cannot follow a multivariate normal distribution by definition, their sample space being the simplex. Consequently, regionalized compositions in general cannot be modeled under explicit or implicit assumptions of multivariate Gaussian processes. Here only the multivariate lognormal and additive logistic normal distributions will be addressed. Besides the logarithmic and additive logratio transformations, others can be applied, such as the multivariate Box-Cox transformation, as stated by Andrews et al. (1971), Rayens and Srinivasan (1991), and Barcelo-Vidal (1996). Furthermore, distributions such as the multiplicative logistic normal distribution introduced by Aitchison (1986, p. 131) or the additive logistic skew-normal distribution defined by Azzalini and Dalla Valle (1996) can be investigated in a similar fashion. References to the literature for the fundamental principles of the theory discussed in this chapter were given in Chapter 2. Among those, special attention is drawn to the work of Myers (1982), where matrix formulation of cokriging was first presented and the properties included in the first section of this chapter were stated.

Estimation variance of dual-rotating-retarder Mueller matrix polarimeter in the presence of Gaussian thermal noise and poisson shot noise

2019 ◽  
Vol 22 (2) ◽  
pp. 025701 ◽  
Author(s):  
Naicheng Quan ◽  
Chunmin Zhang ◽  
Tingkui Mu ◽  
Siyuan Li ◽  
Caiyin You
Keyword(s):  
Shot Noise ◽  
Thermal Noise ◽  
Mueller Matrix ◽  
Estimation Variance ◽  
Poisson Shot Noise
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