scholarly journals Optimal Sampling Schemes for Estimating System Reliability by Testing Components--1: Fixed Sample Sizes

1974 ◽  
Vol 69 (346) ◽  
pp. 485 ◽  
Author(s):  
Donald A. Berry
Keyword(s):  
System Reliability ◽  
Optimal Sampling ◽  
Sample Sizes ◽  
Sampling Schemes ◽  
Fixed Sample

scholarly journals Optimal design for population pk/pd models

2012 ◽  
Vol 51 (1) ◽  
pp. 115-130
Author(s):  
Sergei Leonov ◽  
Alexander Aliev
Keyword(s):  
Optimal Design ◽  
Fisher Information ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Optimal Sampling ◽  
Design Algorithm ◽  
Sampling Schemes ◽  
Population Pk ◽  
Different Types

ABSTRACT We provide some details of the implementation of optimal design algorithm in the PkStaMp library which is intended for constructing optimal sampling schemes for pharmacokinetic (PK) and pharmacodynamic (PD) studies. We discuss different types of approximation of individual Fisher information matrix and describe a user-defined option of the library.

scholarly journals Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

2013 ◽  
Vol 37 (5) ◽  
pp. 1128-1135 ◽  
Author(s):  
Gener Tadeu Pereira ◽  
Zigomar Menezes de Souza ◽  
Daniel De Bortoli Teixeira ◽  
Rafael Montanari ◽  
José Marques Júnior
Keyword(s):  
Soil Properties ◽  
Chemical Property ◽  
Chemical Properties ◽  
Sampling Scheme ◽  
Optimal Sampling ◽  
Sampling Schemes ◽  
Medium Scale ◽  
Chemical Soil ◽  
Physical And Chemical Property ◽  
Physical And Chemical

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

scholarly journals Optimal sampling schemes based on the anticipated variance with lack of fit

2002 ◽  
Vol 32 (12) ◽  
pp. 2236-2243 ◽  
Author(s):  
D Mandallaz
Keyword(s):  
Estimation Procedure ◽  
Cluster Sampling ◽  
Second Phase ◽  
Optimal Sampling ◽  
Two Phase ◽  
Sampling Schemes ◽  
Lack Of Fit ◽  
Important Improvement ◽  
Phase Sampling ◽  
Simple Stochastic Model

This note presents an important improvement for optimal sampling schemes based on the anticipated variance. The anticipated variance is defined as the average of the design-based variance under a simple stochastic model in which the trees are assumed to be uniformly and independently distributed within a given number of so-called Poisson strata. We consider two-phase two-stage cluster sampling schemes in which costs and terrestrial second-phase sampling density can vary over domains. The estimation procedure is based on post-stratification with respect to so-called working strata that do not need to be identical with the Poisson strata, usually unknown, which induces a lack of fit. It is then possible to derive analytically the optimal sampling schemes. Data from the Swiss National Inventory illustrates the method.

Optimal Sampling Schemes

NIR news ◽  
2010 ◽  
Vol 21 (4) ◽  
pp. 11-12
Author(s):  
Tom Fearn
Keyword(s):  
Optimal Sampling ◽  
Sampling Schemes

scholarly journals Comparing Double-Sampling Efficiency Using Various Estimators with Fixed-Area and Point Sampling

2008 ◽  
Vol 25 (2) ◽  
pp. 99-102 ◽  
Author(s):  
Christopher A. Dahl ◽  
Brent A. Harding ◽  
Harry V. Wiant
Keyword(s):  
Double Point ◽  
Double Sampling ◽  
Sampling Efficiency ◽  
Sample Sizes ◽  
Sampling Errors ◽  
Point Sampling ◽  
Sampling Schemes ◽  
Fixed Area ◽  
Multiple Sample ◽  
Volume Estimates

Abstract In this study, we compare the efficiency of double sampling using point sampling and fixed-area plots for sawtimber volume estimates in a mixed-hardwood, oak-dominated stand. Multiple sample sizes and combinations were evaluated to determine optimum double-sample ratios. Results indicatedthat double, point-sampling schemes are more efficient in terms of field time and sampling errors than double-sampling schemes incorporating fixed-area plots. Data suggested that the most efficient ratio of measured and nonmeasured points with double sampling varies on the basis of the nonmeasuredvariable used and desired SE percentage levels for the inventory.

Forest inventory: further results for optimal sampling schemes based on the anticipated variance

2001 ◽  
Vol 31 (10) ◽  
pp. 1845-1853 ◽  
Author(s):  
Daniel Mandallaz ◽  
Adrian Lanz
Keyword(s):  
Forest Inventory ◽  
Optimal Allocation ◽  
Optimality Criterion ◽  
Second Phase ◽  
Optimal Sampling ◽  
Two Stage ◽  
Two Phase ◽  
Sampling Density ◽  
Sampling Schemes ◽  
Inclusion Probabilities

This work presents optimal allocation rules for two-phase, two-stage sampling schemes in which the sampling density and the costs of the second phase can vary over domains. The optimality criterion is based on the anticipated variance. It also gives an improved version of discrete approximation for the resulting inclusion probabilities. An example illustrates the theory.

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