Performance of Various Sampling Schemes in Asymptotic Sampling

Abstract The article examines the use of Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions) have been selected in order to test AS with various sampling schemes: (i) Monte Carlo designs; (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion; (iii) designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.

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Managing Heterogeneity of Variance in Studies of Reliability Generalization With Alpha Coefficients

Methodology ◽

10.1027/1614-2241/a000039 ◽

2012 ◽

Vol 8 (2) ◽

pp. 71-80 ◽

Cited By ~ 3

Author(s):

Juan Botella ◽

Manuel Suero

Keyword(s):

Reliability Estimation ◽

Biased Sampling ◽

Reliability Generalization ◽

Alpha Coefficient ◽

Range Restriction ◽

Sampling Schemes ◽

Cronbach's Alpha ◽

Heterogeneous Variances ◽

Heterogeneity Of Variance ◽

Meta Analyses

In Reliability Generalization (RG) meta-analyses, the importance of bearing in mind the problems of range restriction or biased sampling and their influence on reliability estimation has often been highlighted. Nevertheless, the presence of heterogeneous variances in the included studies has been diagnosed in a subjective way and has not been taken into account in later analyses. Procedures to detect the presence of a variety of sampling schemes and to manage them in the analyses are proposed. The procedures are further explained with an example, by applying them to 25 estimates of Cronbach’s alpha coefficient in the Hamilton Scale for Depression.

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Error analysis of spatial interpolation of soil texture under different sampling schemes

CHINESE JOURNAL OF ECO-AGRICULTURE ◽

10.3724/sp.j.1011.2014.30709 ◽

2014 ◽

Vol 22 (2) ◽

pp. 217-224

Author(s):

Houlong JIANG ◽

Shuduan LIU ◽

Anding XU ◽

Chao YANG

Keyword(s):

Error Analysis ◽

Soil Texture ◽

Spatial Interpolation ◽

Sampling Schemes

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Non-Cartesian sampling schemes and the acquisition of 2D NMR correlation spectra from single-scan experiments

Chemical Physics Letters ◽

10.1016/0009-2614(94)87077-2 ◽

1994 ◽

Vol 222 (4) ◽

pp. 371-377 ◽

Cited By ~ 8

Author(s):

Lucio Frydman ◽

Jian Peng

Keyword(s):

2D Nmr ◽

Sampling Schemes

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Measuring Hydrometeors Using a Precipitation Microphysical Characteristics Sensor: Sampling Effect of Different Bin Sizes on Drop Size Distribution Parameters

Advances in Meteorology ◽

10.1155/2018/9727345 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Xichuan Liu ◽

Taichang Gao ◽

Yuntao Hu ◽

Xiaojian Shu

Keyword(s):

Drop Size ◽

Rain Rate ◽

Monte Carlo Model ◽

Rain Gauge ◽

Sampling Schemes ◽

Distribution Parameters ◽

Optimum Sampling ◽

Simulation Results ◽

The Impact ◽

Good Agreement

In order to improve the measurement of precipitation microphysical characteristics sensor (PMCS), the sampling process of raindrops by PMCS based on a particle-by-particle Monte-Carlo model was simulated to discuss the effect of different bin sizes on DSD measurement, and the optimum sampling bin sizes for PMCS were proposed based on the simulation results. The simulation results of five sampling schemes of bin sizes in four rain-rate categories show that the raw capture DSD has a significant fluctuation variation influenced by the capture probability, whereas the appropriate sampling bin size and width can reduce the impact of variation of raindrop number on DSD shape. A field measurement of a PMCS, an OTT PARSIVEL disdrometer, and a tipping bucket rain Gauge shows that the rain-rate and rainfall accumulations have good consistencies between PMCS, OTT, and Gauge; the DSD obtained by PMCS and OTT has a good agreement; the probability of N0, μ, and Λ shows that there is a good agreement between the Gamma parameters of PMCS and OTT; the fitted μ-Λ and Z-R relationship measured by PMCS is close to that measured by OTT, which validates the performance of PMCS on rain-rate, rainfall accumulation, and DSD related parameters.

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Asymptotic Inference for Optimal Rerandomization Designs

Open Statistics ◽

10.1515/stat-2020-0102 ◽

2021 ◽

Vol 1 (1) ◽

pp. 49-58

Author(s):

Mårten Schultzberg ◽

Per Johansson

Keyword(s):

Experimental Design ◽

Normal Distribution ◽

Mahalanobis Distance ◽

Design Strategy ◽

Sampling Distribution ◽

Optimal Designs ◽

Asymptotic Inference ◽

Optimal Allocations ◽

Asymptotic Sampling

AbstractRecently a computational-based experimental design strategy called rerandomization has been proposed as an alternative or complement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to a balance criterion, and then randomize within the set of acceptable allocations. Based on the Mahalanobis distance criterion for balancing the covariates, we show that asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations. Finally, we show that for the optimal and near optimal designs, the quite complex asymptotic sampling distribution derived by Li et al. (2018), is well approximated by a normal distribution.

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Model-Informed Optimization of a Pediatric Clinical Pharmacokinetic Trial of a New Spironolactone Liquid Formulation

Pharmaceutics ◽

10.3390/pharmaceutics13060849 ◽

2021 ◽

Vol 13 (6) ◽

pp. 849

Author(s):

Manasa Tatipalli ◽

Vijay Kumar Siripuram ◽

Tao Long ◽

Diana Shuster ◽

Galina Bernstein ◽

...

Keyword(s):

Pharmacokinetic Model ◽

Allometric Scaling ◽

Pediatric Patients ◽

Reference Product ◽

Population Pharmacokinetic ◽

Liquid Formulation ◽

Sampling Schemes ◽

Quantitative Pharmacology ◽

Using Data ◽

Drug Pharmacokinetics

Quantitative pharmacology brings important advantages in the design and conduct of pediatric clinical trials. Herein, we demonstrate the application of a model-based approach to select doses and pharmacokinetic sampling scenarios for the clinical evaluation of a novel oral suspension of spironolactone in pediatric patients with edema. A population pharmacokinetic model was developed and qualified for spironolactone and its metabolite, canrenone, using data from adults and bridged to pediatrics (2 to <17 years old) using allometric scaling. The model was then used via simulation to explore different dosing and sampling scenarios. Doses of 0.5 and 1.5 mg/kg led to target exposures (i.e., similar to 25 and 100 mg of the reference product in adults) in all the reference pediatric ages (i.e., 2, 6, 12 and 17 years). Additionally, two different sampling scenarios were delineated to accommodate patients into sparse sampling schemes informative to characterize drug pharmacokinetics while minimizing phlebotomy and burden to participating children.

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Bias correction for direct spectral estimation from irregularly sampled data including sampling schemes with correlation

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-020-00712-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Nils Damaschke ◽

Volker Kühn ◽

Holger Nobach

Keyword(s):

Spectral Estimation ◽

Continuous Distribution ◽

Discrete Distribution ◽

Irregular Sampling ◽

The Other ◽

Sampled Data ◽

Sampling Schemes ◽

Other Hand ◽

Sampling Intervals ◽

Irregularly Sampled Data

AbstractThe prediction and correction of systematic errors in direct spectral estimation from irregularly sampled data taken from a stochastic process is investigated. Different sampling schemes are investigated, which lead to such an irregular sampling of the observed process. Both kinds of sampling schemes are considered, stochastic sampling with non-equidistant sampling intervals from a continuous distribution and, on the other hand, nominally equidistant sampling with missing individual samples yielding a discrete distribution of sampling intervals. For both distributions of sampling intervals, continuous and discrete, different sampling rules are investigated. On the one hand, purely random and independent sampling times are considered. This is given only in those cases, where the occurrence of one sample at a certain time has no influence on other samples in the sequence. This excludes any preferred delay intervals or external selection processes, which introduce correlations between the sampling instances. On the other hand, sampling schemes with interdependency and thus correlation between the individual sampling instances are investigated. This is given whenever the occurrence of one sample in any way influences further sampling instances, e.g., any recovery times after one instance, any preferences of sampling intervals including, e.g., sampling jitter or any external source with correlation influencing the validity of samples. A bias-free estimation of the spectral content of the observed random process from such irregularly sampled data is the goal of this investigation.

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